flatland/utils/misc.py

+# https://stackoverflow.com/questions/715417/converting-from-a-string-to-boolean-in-python
+def str2bool(v):
+    return v.lower() in ("yes", "true", "t", "1")

flatland/utils/ordered_set.py

+# in order for enumeration to be deterministic for testing purposes
+# https://stackoverflow.com/questions/1653970/does-python-have-an-ordered-set
+from collections import OrderedDict
+from collections.abc import MutableSet
+
+
+class OrderedSet(OrderedDict, MutableSet):
+
+    def update(self, *args, **kwargs):
+        if kwargs:
+            raise TypeError("update() takes no keyword arguments")
+
+        for s in args:
+            for e in s:
+                self.add(e)
+
+    def add(self, elem):
+        self[elem] = None
+
+    def discard(self, elem):
+        self.pop(elem, None)
+
+    def __le__(self, other):
+        return all(e in other for e in self)
+
+    def __lt__(self, other):
+        return self <= other and self != other
+
+    def __ge__(self, other):
+        return all(e in self for e in other)
+
+    def __gt__(self, other):
+        return self >= other and self != other
+
+    def __repr__(self):
+        return 'OrderedSet([%s])' % (', '.join(map(repr, self.keys())))
+
+    def __str__(self):
+        return '{%s}' % (', '.join(map(repr, self.keys())))
+
+    difference = property(lambda self: self.__sub__)
+    difference_update = property(lambda self: self.__isub__)
+    intersection = property(lambda self: self.__and__)
+    intersection_update = property(lambda self: self.__iand__)
+    issubset = property(lambda self: self.__le__)
+    issuperset = property(lambda self: self.__ge__)
+    symmetric_difference = property(lambda self: self.__xor__)
+    symmetric_difference_update = property(lambda self: self.__ixor__)
+    union = property(lambda self: self.__or__)

flatland/utils/rendertools.py

 import time
+import warnings
 from collections import deque
 from enum import IntEnum
 
@@ -6,7 +7,10 @@ import numpy as np
 from numpy import array
 from recordtype import recordtype
 
+from flatland.envs.step_utils.states import TrainState
+
 from flatland.utils.graphics_pil import PILGL, PILSVG
+from flatland.utils.graphics_pgl import PGLGL
 
 
 # TODO: suggested renaming to RailEnvRenderTool, as it will only work with RailEnv!
@@ -20,51 +24,171 @@ class AgentRenderVariant(IntEnum):
 
 
 class RenderTool(object):
+    """ RenderTool is a facade to a renderer.
+        (This was introduced for the Browser / JS renderer which has now been removed.)
+    """
+    def __init__(self, env, gl="PGL", jupyter=False,
+                 agent_render_variant=AgentRenderVariant.ONE_STEP_BEHIND,
+                 show_debug=False, clear_debug_text=True, screen_width=800, screen_height=600,
+                 host="localhost", port=None):
+
+        self.env = env
+        self.frame_nr = 0
+        self.start_time = time.time()
+        self.times_list = deque()
+
+        self.agent_render_variant = agent_render_variant
+
+        if gl in ["PIL", "PILSVG", "PGL"]:
+            self.renderer = RenderLocal(env, gl, jupyter,
+                 agent_render_variant,
+                 show_debug, clear_debug_text, screen_width, screen_height)
+            self.gl = self.renderer.gl
+        else:
+            print("[", gl, "] not found, switch to PGL")
+
+    def render_env(self,
+                   show=False,  # whether to call matplotlib show() or equivalent after completion
+                   show_agents=True,  # whether to include agents
+                   show_inactive_agents=False,  # whether to show agents before they start
+                   show_observations=True,  # whether to include observations
+                   show_predictions=False,  # whether to include predictions
+                   show_rowcols=False, # label the rows and columns
+                   frames=False,  # frame counter to show (intended since invocation)
+                   episode=None,  # int episode number to show
+                   step=None,  # int step number to show in image
+                   selected_agent=None,  # indicate which agent is "selected" in the editor):
+                   return_image=False): # indicate if image is returned for use in monitor:
+        return self.renderer.render_env(show, show_agents, show_inactive_agents, show_observations,
+                    show_predictions, show_rowcols, frames, episode, step, selected_agent, return_image)
+
+    def close_window(self):
+        self.renderer.close_window()
+
+    def reset(self):
+        self.renderer.reset()
+    
+    def set_new_rail(self):
+        self.renderer.set_new_rail()
+        self.renderer.env = self.env  # bit of a hack - copy our env to the delegate
+
+    def update_background(self):
+        self.renderer.update_background()
+    
+    def get_endpoint_URL(self):
+        """ Returns a string URL for the root of the HTTP server
+            TODO: Need to update this work work on a remote server!  May be tricky...
+        """
+        #return "http://localhost:{}".format(self.renderer.get_port())
+        if hasattr(self.renderer, "get_endpoint_url"):
+            return self.renderer.get_endpoint_url()
+        else:
+            print("Attempt to get_endpoint_url from RenderTool - only supported with BROWSER")
+            return None
+
+    def get_image(self):
+        """ 
+        """
+        if hasattr(self.renderer, "gl"):
+            return self.renderer.gl.get_image()
+        else:
+            print("Attempt to retrieve image from RenderTool - not supported with BROWSER")
+            return None
+
+
+class RenderBase(object):
+    def __init__(self, env):
+        pass
+
+    def render_env(self):
+        pass
+
+    def close_window(self):
+        pass
+
+    def reset(self):
+        pass
+
+    def set_new_rail(self):
+        """ Signal to the renderer that the env has changed and will need re-rendering.
+        """
+        pass
+
+    def update_background(self):
+        """ A lesser version of set_new_rail?  
+            TODO: can update_background be pruned for simplicity?
+        """
+        pass
+
+
+
+class RenderLocal(RenderBase):
     """ Class to render the RailEnv and agents.
         Uses two layers, layer 0 for rails (mostly static), layer 1 for agents etc (dynamic)
         The lower / rail layer 0 is only redrawn after set_new_rail() has been called.
         Created with a "GraphicsLayer" or gl - now either PIL or PILSVG
     """
-    Visit = recordtype("Visit", ["rc", "iDir", "iDepth", "prev"])
+    visit = recordtype("visit", ["rc", "iDir", "iDepth", "prev"])
 
-    lColors = list("brgcmyk")
+    color_list = list("brgcmyk")
     # \delta RC for NESW
-    gTransRC = np.array([[-1, 0], [0, 1], [1, 0], [0, -1]])
-    nPixCell = 1  # misnomer...
-    nPixHalf = nPixCell / 2
-    xyHalf = array([nPixHalf, -nPixHalf])
-    grc2xy = array([[0, -nPixCell], [nPixCell, 0]])
-    gGrid = array(np.meshgrid(np.arange(10), -np.arange(10))) * array([[[nPixCell]], [[nPixCell]]])
-    gTheta = np.linspace(0, np.pi / 2, 5)
-    gArc = array([np.cos(gTheta), np.sin(gTheta)]).T  # from [1,0] to [0,1]
-
-    def __init__(self, env, gl="PILSVG", jupyter=False, agentRenderVariant=AgentRenderVariant.ONE_STEP_BEHIND):
+    transitions_row_col = np.array([[-1, 0], [0, 1], [1, 0], [0, -1]])
+    pix_per_cell = 1  # misnomer...
+    half_pix_per_cell = pix_per_cell / 2
+    x_y_half = array([half_pix_per_cell, -half_pix_per_cell])
+    row_col_to_xy = array([[0, -pix_per_cell], [pix_per_cell, 0]])
+    grid = array(np.meshgrid(np.arange(10), -np.arange(10))) * array([[[pix_per_cell]], [[pix_per_cell]]])
+    theta = np.linspace(0, np.pi / 2, 5)
+    arc = array([np.cos(theta), np.sin(theta)]).T  # from [1,0] to [0,1]
+
+    def __init__(self, env, gl="PILSVG", jupyter=False,
+                 agent_render_variant=AgentRenderVariant.ONE_STEP_BEHIND,
+                 show_debug=False, clear_debug_text=True, screen_width=800, screen_height=600):
+
         self.env = env
-        self.iFrame = 0
-        self.time1 = time.time()
-        self.lTimes = deque()
+        self.frame_nr = 0
+        self.start_time = time.time()
+        self.times_list = deque()
+
+        self.agent_render_variant = agent_render_variant
 
-        self.agentRenderVariant = agentRenderVariant
+        self.gl_str = gl
 
         if gl == "PIL":
-            self.gl = PILGL(env.width, env.height, jupyter)
+            self.gl = PILGL(env.width, env.height, jupyter, screen_width=screen_width, screen_height=screen_height)
         elif gl == "PILSVG":
-            self.gl = PILSVG(env.width, env.height, jupyter)
+            self.gl = PILSVG(env.width, env.height, jupyter, screen_width=screen_width, screen_height=screen_height)
         else:
-            print("[", gl, "] not found, switch to PILSVG")
-            self.gl = PILSVG(env.width, env.height, jupyter)
+            if gl != "PGL":
+                print("[", gl, "] not found, switch to PGL, PILSVG")
+                print("Using PGL")
+            self.gl = PGLGL(env.width, env.height, jupyter, screen_width=screen_width, screen_height=screen_height)
 
         self.new_rail = True
+        self.show_debug = show_debug
+        self.clear_debug_text = clear_debug_text
         self.update_background()
 
+    def reset(self):
+        """
+        Resets the environment
+        :return:
+        """
+        self.set_new_rail()
+        self.frame_nr = 0
+        self.start_time = time.time()
+        self.times_list = deque()
+        return
+
     def update_background(self):
         # create background map
-        dTargets = {}
-        for iAgent, agent in enumerate(self.env.agents_static):
+        targets = {}
+        for agent_idx, agent in enumerate(self.env.agents):
             if agent is None:
                 continue
-            dTargets[tuple(agent.target)] = iAgent
-        self.gl.build_background_map(dTargets)
+            #print(f"updatebg: {agent_idx} {agent.target}")
+            targets[tuple(agent.target)] = agent_idx
+        self.gl.build_background_map(targets)
 
     def resize(self):
         self.gl.resize(self.env)
@@ -75,47 +199,30 @@ class RenderTool(object):
         """
         self.new_rail = True
 
-    def plotTreeOnRail(self, lVisits, color="r"):
-        """
-        DEFUNCT
-        Derives and plots a tree of transitions starting at position rcPos
-        in direction iDir.
-        Returns a list of Visits which are the nodes / vertices in the tree.
-        """
-        rt = self.__class__
-
-        for visit in lVisits:
-            # transition for next cell
-            tbTrans = self.env.rail.get_transitions(*visit.rc, visit.iDir)
-            giTrans = np.where(tbTrans)[0]  # RC list of transitions
-            gTransRCAg = rt.gTransRC[giTrans]
-            self.plotTrans(visit.rc, gTransRCAg, depth=str(visit.iDepth), color=color)
+    def plot_agents(self, targets=True, selected_agent=None):
+        color_map = self.gl.get_cmap('hsv', lut=(len(self.env.agents) + 1))
 
-    def plotAgents(self, targets=True, iSelectedAgent=None):
-        cmap = self.gl.get_cmap('hsv',
-                                lut=max(len(self.env.agents), len(self.env.agents_static) + 1))
-
-        for iAgent, agent in enumerate(self.env.agents_static):
+        for agent_idx, agent in enumerate(self.env.agents):
             if agent is None:
                 continue
-            oColor = cmap(iAgent)
-            self.plotAgent(agent.position, agent.direction, oColor, target=agent.target if targets else None,
-                           static=True, selected=iAgent == iSelectedAgent)
+            color = color_map(agent_idx)
+            self.plot_single_agent(agent.position, agent.direction, color, target=agent.target if targets else None,
+                                   static=True, selected=agent_idx == selected_agent)
 
-        for iAgent, agent in enumerate(self.env.agents):
+        for agent_idx, agent in enumerate(self.env.agents):
             if agent is None:
                 continue
-            oColor = cmap(iAgent)
-            self.plotAgent(agent.position, agent.direction, oColor, target=agent.target if targets else None)
+            color = color_map(agent_idx)
+            self.plot_single_agent(agent.position, agent.direction, color, target=agent.target if targets else None)
 
-    def getTransRC(self, rcPos, iDir, bgiTrans=False):
+    def get_transition_row_col(self, row_col_pos, direction, bgiTrans=False):
         """
-        Get the available transitions for rcPos in direction iDir,
+        Get the available transitions for row_col_pos in direction direction,
         as row & col deltas.
 
         If bgiTrans is True, return a grid of indices of available transitions.
 
-        eg for a cell rcPos = (4,5), in direction iDir = 0 (N),
+        eg for a cell row_col_pos = (4,5), in direction direction = 0 (N),
         where the available transitions are N and E, returns:
         [[-1,0], [0,1]] ie N=up one row, and E=right one col.
         and if bgiTrans is True, returns a tuple:
@@ -125,217 +232,78 @@ class RenderTool(object):
         )
         """
 
-        tbTrans = self.env.rail.get_transitions(*rcPos, iDir)
-        giTrans = np.where(tbTrans)[0]  # RC list of transitions
+        transitions = self.env.rail.get_transitions(*row_col_pos, direction)
+        transition_list = np.where(transitions)[0]  # RC list of transitions
 
         # HACK: workaround dead-end transitions
-        if len(giTrans) == 0:
-            iDirReverse = (iDir + 2) % 4
-            tbTrans = tuple(int(iDir2 == iDirReverse) for iDir2 in range(4))
-            giTrans = np.where(tbTrans)[0]  # RC list of transitions
+        if len(transition_list) == 0:
+            reverse_direciton = (direction + 2) % 4
+            transitions = tuple(int(tmp_dir == reverse_direciton) for tmp_dir in range(4))
+            transition_list = np.where(transitions)[0]  # RC list of transitions
 
-        gTransRCAg = self.__class__.gTransRC[giTrans]
+        transition_grid = self.__class__.transitions_row_col[transition_list]
 
         if bgiTrans:
-            return gTransRCAg, giTrans
+            return transition_grid, transition_list
         else:
-            return gTransRCAg
+            return transition_grid
 
-    def plotAgent(self, rcPos, iDir, color="r", target=None, static=False, selected=False):
+    def plot_single_agent(self, position_row_col, direction, color="r", target=None, static=False, selected=False):
         """
         Plot a simple agent.
         Assumes a working graphics layer context (cf a MPL figure).
         """
+        if position_row_col is None:
+            return
+
         rt = self.__class__
 
-        rcDir = rt.gTransRC[iDir]  # agent direction in RC
-        xyDir = np.matmul(rcDir, rt.grc2xy)  # agent direction in xy
+        direction_row_col = rt.transitions_row_col[direction]  # agent direction in RC
+        direction_xy = np.matmul(direction_row_col, rt.row_col_to_xy)  # agent direction in xy
 
-        xyPos = np.matmul(rcPos - rcDir / 2, rt.grc2xy) + rt.xyHalf
+        xyPos = np.matmul(position_row_col - direction_row_col / 2, rt.row_col_to_xy) + rt.x_y_half
 
         if static:
-            color = self.gl.adaptColor(color, lighten=True)
+            color = self.gl.adapt_color(color, lighten=True)
 
         color = color
 
         self.gl.scatter(*xyPos, color=color, layer=1, marker="o", s=100)  # agent location
-        xyDirLine = array([xyPos, xyPos + xyDir / 2]).T  # line for agent orient.
-        self.gl.plot(*xyDirLine, color=color, layer=1, lw=5, ms=0, alpha=0.6)
+        xy_dir_line = array([xyPos, xyPos + direction_xy / 2]).T  # line for agent orient.
+        self.gl.plot(*xy_dir_line, color=color, layer=1, lw=5, ms=0, alpha=0.6)
         if selected:
             self._draw_square(xyPos, 1, color)
 
         if target is not None:
-            rcTarget = array(target)
-            xyTarget = np.matmul(rcTarget, rt.grc2xy) + rt.xyHalf
-            self._draw_square(xyTarget, 1 / 3, color, layer=1)
+            target_row_col = array(target)
+            target_xy = np.matmul(target_row_col, rt.row_col_to_xy) + rt.x_y_half
+            self._draw_square(target_xy, 1 / 3, color, layer=1)
 
-    def plotTrans(self, rcPos, gTransRCAg, color="r", depth=None):
+    def plot_transition(self, position_row_col, transition_row_col, color="r", depth=None):
         """
-        plot the transitions in gTransRCAg at position rcPos.
-        gTransRCAg is a 2d numpy array containing a list of RC transitions,
+        plot the transitions in transition_row_col at position position_row_col.
+        transition_row_col is a 2d numpy array containing a list of RC transitions,
         eg [[-1,0], [0,1]] means N, E.
 
         """
 
         rt = self.__class__
-        xyPos = np.matmul(rcPos, rt.grc2xy) + rt.xyHalf
-        gxyTrans = xyPos + np.matmul(gTransRCAg, rt.grc2xy / 2.4)
-        self.gl.scatter(*gxyTrans.T, color=color, marker="o", s=50, alpha=0.2)
+        position_xy = np.matmul(position_row_col, rt.row_col_to_xy) + rt.x_y_half
+        transition_xy = position_xy + np.matmul(transition_row_col, rt.row_col_to_xy / 2.4)
+        self.gl.scatter(*transition_xy.T, color=color, marker="o", s=50, alpha=0.2)
         if depth is not None:
-            for x, y in gxyTrans:
+            for x, y in transition_xy:
                 self.gl.text(x, y, depth)
 
-    def getTreeFromRail(self, rcPos, iDir, nDepth=10, bBFS=True, bPlot=False):
-        """
-        DEFUNCT
-        Generate a tree from the env starting at rcPos, iDir.
-        """
-        rt = self.__class__
-        print(rcPos, iDir)
-        iPos = 0 if bBFS else -1  # BF / DF Search
-
-        iDepth = 0
-        visited = set()
-        lVisits = []
-        stack = [rt.Visit(rcPos, iDir, iDepth, None)]
-        while stack:
-            visit = stack.pop(iPos)
-            rcd = (visit.rc, visit.iDir)
-            if visit.iDepth > nDepth:
-                continue
-            lVisits.append(visit)
-
-            if rcd not in visited:
-                visited.add(rcd)
-
-                gTransRCAg, giTrans = self.getTransRC(visit.rc,
-                                                      visit.iDir,
-                                                      bgiTrans=True)
-                # enqueue the next nodes (ie transitions from this node)
-                for gTransRC2, iTrans in zip(gTransRCAg, giTrans):
-                    visitNext = rt.Visit(tuple(visit.rc + gTransRC2),
-                                         iTrans,
-                                         visit.iDepth + 1,
-                                         visit)
-                    stack.append(visitNext)
-
-                # plot the available transitions from this node
-                if bPlot:
-                    self.plotTrans(
-                        visit.rc, gTransRCAg,
-                        depth=str(visit.iDepth))
-
-        return lVisits
-
-    def plotTree(self, lVisits, xyTarg):
-        '''
-        Plot a vertical tree of transitions.
-        Returns the "visit" to the destination
-        (ie where euclidean distance is near zero) or None if absent.
-        '''
-
-        dPos = {}
-        iPos = 0
-
-        visitDest = None
-
-        for iVisit, visit in enumerate(lVisits):
-
-            if visit.rc in dPos:
-                xLoc = dPos[visit.rc]
-            else:
-                xLoc = dPos[visit.rc] = iPos
-                iPos += 1
-
-            rDist = np.linalg.norm(array(visit.rc) - array(xyTarg))
-
-            xLoc = rDist + visit.iDir / 4
-
-            # point labelled with distance
-            self.gl.scatter(xLoc, visit.iDepth, color="k", s=2)
-            self.gl.text(xLoc, visit.iDepth, visit.rc, color="k", rotation=45)
-
-            # if len(dPos)>1:
-            if visit.prev:
-                xLocPrev = dPos[visit.prev.rc]
-
-                rDistPrev = np.linalg.norm(array(visit.prev.rc) -
-                                           array(xyTarg))
-
-                xLocPrev = rDistPrev + visit.prev.iDir / 4
-
-                # line from prev node
-                self.gl.plot([xLocPrev, xLoc],
-                             [visit.iDepth - 1, visit.iDepth],
-                             color="k", alpha=0.5, lw=1)
-
-            if rDist < 0.1:
-                visitDest = visit
-
-        # Walk backwards from destination to origin, plotting in red
-        if visitDest is not None:
-            visit = visitDest
-            xLocPrev = None
-            while visit is not None:
-                rDist = np.linalg.norm(array(visit.rc) - array(xyTarg))
-                xLoc = rDist + visit.iDir / 4
-                if xLocPrev is not None:
-                    self.gl.plot([xLoc, xLocPrev], [visit.iDepth, visit.iDepth + 1],
-                                 color="r", alpha=0.5, lw=2)
-                xLocPrev = xLoc
-                visit = visit.prev
-
-        self.gl.prettify()
-        return visitDest
-
-    def plotPath(self, visitDest):
-        """
-        Given a "final" visit visitDest, plotPath recurses back through the path
-        using the visit.prev field (previous) to get back to the start of the path.
-        The path of transitions is plotted with arrows at 3/4 along the line.
-        The transition is plotted slightly to one side of the rail, so that
-        transitions in opposite directions are separate.
-        Currently, no attempt is made to make the transition arrows coincide
-        at corners, and they are straight only.
-        """
-
-        rt = self.__class__
-        # Walk backwards from destination to origin
-        if visitDest is not None:
-            visit = visitDest
-            xyPrev = None
-            while visit is not None:
-                xy = np.matmul(visit.rc, rt.grc2xy) + rt.xyHalf
-                if xyPrev is not None:
-                    dx, dy = (xyPrev - xy) / 20
-                    xyLine = array([xy, xyPrev]) + array([dy, dx])
-
-                    self.gl.plot(*xyLine.T, color="r", alpha=0.5, lw=1)
-
-                    xyMid = np.sum(xyLine * [[1 / 4], [3 / 4]], axis=0)
-
-                    xyArrow = array([
-                        xyMid + [-dx - dy, +dx - dy],
-                        xyMid,
-                        xyMid + [-dx + dy, -dx - dy]])
-                    self.gl.plot(*xyArrow.T, color="r")
-
-                visit = visit.prev
-                xyPrev = xy
-
-    def drawTrans(self, oFrom, oTo, sColor="gray"):
-        self.gl.plot(
-            [oFrom[0], oTo[0]],  # x
-            [oFrom[1], oTo[1]],  # y
-            color=sColor
-        )
-
-    def drawTrans2(self,
-                   xyLine, xyCentre,
-                   rotation, bDeadEnd=False,
-                   sColor="gray",
-                   bArrow=True,
-                   spacing=0.1):
+    def draw_transition(self,
+                        line,
+                        center,
+                        rotation,
+                        dead_end=False,
+                        curves=False,
+                        color="gray",
+                        arrow=True,
+                        spacing=0.1):
         """
         gLine is a numpy 2d array of points,
         in the plotting space / coords.
@@ -344,67 +312,77 @@ class RenderTool(object):
         from x=0, y=0.5
         to   x=1, y=0.2
         """
-        rt = self.__class__
-        bStraight = rotation in [0, 2]
-        dx, dy = np.squeeze(np.diff(xyLine, axis=0)) * spacing / 2
 
-        if bStraight:
+        if not curves and not dead_end:
+            # just a straigt line, no curve nor dead_end included in this basic rail element
+            self.gl.plot(
+                [line[0][0], line[1][0]],  # x
+                [line[0][1], line[1][1]],  # y
+                color=color
+            )
+        else:
+            # it was not a simple line to draw: the rail has a curve or dead_end included.
+            rt = self.__class__
+            straight = rotation in [0, 2]
+            dx, dy = np.squeeze(np.diff(line, axis=0)) * spacing / 2
 
-            if sColor == "auto":
-                if dx > 0 or dy > 0:
-                    sColor = "C1"  # N or E
+            if straight:
+
+                if color == "auto":
+                    if dx > 0 or dy > 0:
+                        color = "C1"  # N or E
+                    else:
+                        color = "C2"  # S or W
+
+                if dead_end:
+                    line_xy = array([
+                        line[1] + [dy, dx],
+                        center,
+                        line[1] - [dy, dx],
+                    ])
+                    self.gl.plot(*line_xy.T, color=color)
                 else:
-                    sColor = "C2"  # S or W
-
-            if bDeadEnd:
-                xyLine2 = array([
-                    xyLine[1] + [dy, dx],
-                    xyCentre,
-                    xyLine[1] - [dy, dx],
-                ])
-                self.gl.plot(*xyLine2.T, color=sColor)
-            else:
-                xyLine2 = xyLine + [-dy, dx]
-                self.gl.plot(*xyLine2.T, color=sColor)
+                    line_xy = line + [-dy, dx]
+                    self.gl.plot(*line_xy.T, color=color)
 
-                if bArrow:
-                    xyMid = np.sum(xyLine2 * [[1 / 4], [3 / 4]], axis=0)
+                    if arrow:
+                        middle_xy = np.sum(line_xy * [[1 / 4], [3 / 4]], axis=0)
 
-                    xyArrow = array([
-                        xyMid + [-dx - dy, +dx - dy],
-                        xyMid,
-                        xyMid + [-dx + dy, -dx - dy]])
-                    self.gl.plot(*xyArrow.T, color=sColor)
+                        arrow_xy = array([
+                            middle_xy + [-dx - dy, +dx - dy],
+                            middle_xy,
+                            middle_xy + [-dx + dy, -dx - dy]])
+                        self.gl.plot(*arrow_xy.T, color=color)
 
-        else:
-
-            xyMid = np.mean(xyLine, axis=0)
-            dxy = xyMid - xyCentre
-            xyCorner = xyMid + dxy
-            if rotation == 1:
-                rArcFactor = 1 - spacing
-                sColorAuto = "C1"
             else:
-                rArcFactor = 1 + spacing
-                sColorAuto = "C2"
-            dxy2 = (xyCentre - xyCorner) * rArcFactor  # for scaling the arc
-
-            if sColor == "auto":
-                sColor = sColorAuto
-
-            self.gl.plot(*(rt.gArc * dxy2 + xyCorner).T, color=sColor)
-
-            if bArrow:
-                dx, dy = np.squeeze(np.diff(xyLine, axis=0)) / 20
-                iArc = int(len(rt.gArc) / 2)
-                xyMid = xyCorner + rt.gArc[iArc] * dxy2
-                xyArrow = array([
-                    xyMid + [-dx - dy, +dx - dy],
-                    xyMid,
-                    xyMid + [-dx + dy, -dx - dy]])
-                self.gl.plot(*xyArrow.T, color=sColor)
-
-    def renderObs(self, agent_handles, observation_dict):
+
+                middle_xy = np.mean(line, axis=0)
+                dxy = middle_xy - center
+                corner = middle_xy + dxy
+                if rotation == 1:
+                    arc_factor = 1 - spacing
+                    color_auto = "C1"
+                else:
+                    arc_factor = 1 + spacing
+                    color_auto = "C2"
+                dxy2 = (center - corner) * arc_factor  # for scaling the arc
+
+                if color == "auto":
+                    color = color_auto
+
+                self.gl.plot(*(rt.arc * dxy2 + corner).T, color=color)
+
+                if arrow:
+                    dx, dy = np.squeeze(np.diff(line, axis=0)) / 20
+                    iArc = int(len(rt.arc) / 2)
+                    middle_xy = corner + rt.arc[iArc] * dxy2
+                    arrow_xy = array([
+                        middle_xy + [-dx - dy, +dx - dy],
+                        middle_xy,
+                        middle_xy + [-dx + dy, -dx - dy]])
+                    self.gl.plot(*arrow_xy.T, color=color)
+
+    def render_observation(self, agent_handles, observation_dict):
         """
         Render the extent of the observation of each agent. All cells that appear in the agent
         observation will be highlighted.
@@ -414,38 +392,72 @@ class RenderTool(object):
         """
         rt = self.__class__
 
-        for agent in agent_handles:
-            color = self.gl.getAgentColor(agent)
-            for visited_cell in observation_dict[agent]:
-                cell_coord = array(visited_cell[:2])
-                cell_coord_trans = np.matmul(cell_coord, rt.grc2xy) + rt.xyHalf
-                self._draw_square(cell_coord_trans, 1 / (agent + 1.1), color, layer=1, opacity=100)
+        # Check if the observation builder provides an observation
+        if len(observation_dict) < 1:
+            warnings.warn(
+                "Predictor did not provide any predicted cells to render. \
+                Observation builder needs to populate: env.dev_obs_dict")
+        else:
+            for agent in agent_handles:
+                color = self.gl.get_agent_color(agent)
+                for visited_cell in observation_dict[agent]:
+                    cell_coord = array(visited_cell[:2])
+                    cell_coord_trans = np.matmul(cell_coord, rt.row_col_to_xy) + rt.x_y_half
+                    self._draw_square(cell_coord_trans, 1 / (agent + 1.1), color, layer=1, opacity=100)
+
+    def render_prediction(self, agent_handles, prediction_dict):
+        """
+        Render the extent of the observation of each agent. All cells that appear in the agent
+        observation will be highlighted.
+        :param agent_handles: List of agent indices to adapt color and get correct observation
+        :param observation_dict: dictionary containing sets of cells of the agent observation
+
+        """
+        rt = self.__class__
+        if len(prediction_dict) < 1:
+            warnings.warn(
+                "Predictor did not provide any predicted cells to render. \
+                Predictors builder needs to populate: env.dev_pred_dict")
+        else:
+            for agent in agent_handles:
+                color = self.gl.get_agent_color(agent)
+                for visited_cell in prediction_dict[agent]:
+                    cell_coord = array(visited_cell[:2])
+                    if type(self.gl) is PILSVG:
+                        # TODO : Track highlighting (Adrian)
+                        r = cell_coord[0]
+                        c = cell_coord[1]
+                        transitions = self.env.rail.grid[r, c]
+                        self.gl.set_predicion_path_at(r, c, transitions, agent_rail_color=color)
+                    else:
+                        cell_coord_trans = np.matmul(cell_coord, rt.row_col_to_xy) + rt.x_y_half
+                        self._draw_square(cell_coord_trans, 1 / (agent + 1.1), color, layer=1, opacity=100)
 
-    def renderRail(self, spacing=False, sRailColor="gray", curves=True, arrows=False):
+    def render_rail(self, spacing=False, rail_color="gray", curves=True, arrows=False):
 
         cell_size = 1  # TODO: remove cell_size
         env = self.env
 
         # Draw cells grid
         grid_color = [0.95, 0.95, 0.95]
-        for r in range(env.height + 1):
+        for row in range(env.height + 1):
             self.gl.plot([0, (env.width + 1) * cell_size],
-                         [-r * cell_size, -r * cell_size],
+                         [-row * cell_size, -row * cell_size],
                          color=grid_color, linewidth=2)
-        for c in range(env.width + 1):
-            self.gl.plot([c * cell_size, c * cell_size],
+        for col in range(env.width + 1):
+            self.gl.plot([col * cell_size, col * cell_size],
                          [0, -(env.height + 1) * cell_size],
                          color=grid_color, linewidth=2)
 
         # Draw each cell independently
-        for r in range(env.height):
-            for c in range(env.width):
+        for row in range(env.height):
+            for col in range(env.width):
 
                 # bounding box of the grid cell
-                x0 = cell_size * c  # left
-                x1 = cell_size * (c + 1)  # right
-                y0 = cell_size * -r  # top
-                y1 = cell_size * -(r + 1)  # bottom
+                x0 = cell_size * col  # left
+                x1 = cell_size * (col + 1)  # right
+                y0 = cell_size * -row  # top
+                y1 = cell_size * -(row + 1)  # bottom
 
                 # centres of cell edges
                 coords = [
@@ -456,16 +468,16 @@ class RenderTool(object):
                 ]
 
                 # cell centre
-                xyCentre = array([x0, y1]) + cell_size / 2
+                center_xy = array([x0, y1]) + cell_size / 2
 
                 # cell transition values
-                oCell = env.rail.get_full_transitions(r, c)
+                cell = env.rail.get_full_transitions(row, col)
 
-                bCellValid = env.rail.cell_neighbours_valid((r, c), check_this_cell=True)
+                cell_valid = env.rail.cell_neighbours_valid((row, col), check_this_cell=True)
 
                 # Special Case 7, with a single bit; terminate at center
                 nbits = 0
-                tmp = oCell
+                tmp = cell
 
                 while tmp > 0:
                     nbits += (tmp & 1)
@@ -473,110 +485,128 @@ class RenderTool(object):
 
                 # as above - move the from coord to the centre
                 # it's a dead env.
-                bDeadEnd = nbits == 1
+                is_dead_end = nbits == 1
 
-                if not bCellValid:
-                    self.gl.scatter(*xyCentre, color="r", s=30)
+                if not cell_valid:
+                    self.gl.scatter(*center_xy, color="r", s=30)
 
                 for orientation in range(4):  # ori is where we're heading
                     from_ori = (orientation + 2) % 4  # 0123=NESW -> 2301=SWNE
                     from_xy = coords[from_ori]
 
-                    tMoves = env.rail.get_transitions(r, c, orientation)
+                    moves = env.rail.get_transitions(row, col, orientation)
 
                     for to_ori in range(4):
                         to_xy = coords[to_ori]
                         rotation = (to_ori - from_ori) % 4
-
-                        if (tMoves[to_ori]):  # if we have this transition
-
-                            if bDeadEnd:
-                                self.drawTrans2(
-                                    array([from_xy, to_xy]), xyCentre,
-                                    rotation, bDeadEnd=True, spacing=spacing,
-                                    sColor=sRailColor)
-
-                            else:
-
-                                if curves:
-                                    self.drawTrans2(
-                                        array([from_xy, to_xy]), xyCentre,
-                                        rotation, spacing=spacing, bArrow=arrows,
-                                        sColor=sRailColor)
-                                else:
-                                    self.drawTrans(self, from_xy, to_xy, sRailColor)
-
-                            if False:
-                                print(
-                                    "r,c,ori: ", r, c, orientation,
-                                    "cell:", "{0:b}".format(oCell),
-                                    "moves:", tMoves,
-                                    "from:", from_ori, from_xy,
-                                    "to: ", to_ori, to_xy,
-                                    "cen:", *xyCentre,
-                                    "rot:", rotation,
-                                )
-
-    def renderEnv(self,
-                  show=False,  # whether to call matplotlib show() or equivalent after completion
-                  # use false when calling from Jupyter.  (and matplotlib no longer supported!)
-                  curves=True,  # draw turns as curves instead of straight diagonal lines
-                  spacing=False,  # defunct - size of spacing between rails
-                  arrows=False,  # defunct - draw arrows on rail lines
-                  agents=True,  # whether to include agents
-                  show_observations=True,  # whether to include observations
-                  sRailColor="gray",  # color to use in drawing rails (not used with SVG)
-                  frames=False,  # frame counter to show (intended since invocation)
-                  iEpisode=None,  # int episode number to show
-                  iStep=None,  # int step number to show in image
-                  iSelectedAgent=None,  # indicate which agent is "selected" in the editor
-                  action_dict=None):  # defunct - was used to indicate agent intention to turn
+                        if (moves[to_ori]):  # if we have this transition
+                            self.draw_transition(
+                                array([from_xy, to_xy]), center_xy,
+                                rotation, dead_end=is_dead_end, curves=curves and not is_dead_end, spacing=spacing,
+                                color=rail_color)
+
+    def render_env(self,
+                   show=False,  # whether to call matplotlib show() or equivalent after completion
+                   show_agents=True,  # whether to include agents
+                   show_inactive_agents=False,
+                   show_observations=True,  # whether to include observations
+                   show_predictions=False,  # whether to include predictions
+                   show_rowcols=False,  # label the rows and columns
+                   frames=False,  # frame counter to show (intended since invocation)
+                   episode=None,  # int episode number to show
+                   step=None,  # int step number to show in image
+                   selected_agent=None,  # indicate which agent is "selected" in the editor
+                   return_image=False): # indicate if image is returned for use in monitor:
         """ Draw the environment using the GraphicsLayer this RenderTool was created with.
             (Use show=False from a Jupyter notebook with %matplotlib inline)
         """
 
-        if not self.gl.is_raster():
-            self.renderEnv2(show=show, curves=curves, spacing=spacing,
-                            arrows=arrows, agents=agents, show_observations=show_observations,
-                            sRailColor=sRailColor,
-                            frames=frames, iEpisode=iEpisode, iStep=iStep,
-                            iSelectedAgent=iSelectedAgent, action_dict=action_dict)
-            return
+        # if type(self.gl) is PILSVG:
+        if self.gl_str in ["PILSVG", "PGL"]:
+            return self.render_env_svg(show=show,
+                                show_observations=show_observations,
+                                show_predictions=show_predictions,
+                                selected_agent=selected_agent,
+                                show_agents=show_agents,
+                                show_inactive_agents=show_inactive_agents,
+                                show_rowcols=show_rowcols,
+                                return_image=return_image
+                                )
+        else:
+            return self.render_env_pil(show=show,
+                                show_agents=show_agents,
+                                show_inactive_agents=show_inactive_agents,
+                                show_observations=show_observations,
+                                show_predictions=show_predictions,
+                                show_rowcols=show_rowcols,
+                                frames=frames,
+                                episode=episode,
+                                step=step,
+                                selected_agent=selected_agent,
+                                return_image=return_image
+                                )
+
+    def _draw_square(self, center, size, color, opacity=255, layer=0):
+        x0 = center[0] - size / 2
+        x1 = center[0] + size / 2
+        y0 = center[1] - size / 2
+        y1 = center[1] + size / 2
+        self.gl.plot([x0, x1, x1, x0, x0], [y0, y0, y1, y1, y0], color=color, layer=layer, opacity=opacity)
+
+    def get_image(self):
+        return self.gl.get_image()
+
+    def render_env_pil(self,
+                       show=False,  # whether to call matplotlib show() or equivalent after completion
+                       # use false when calling from Jupyter.  (and matplotlib no longer supported!)
+                       show_agents=True,  # whether to include agents
+                       show_inactive_agents=False, 
+                       show_observations=True,  # whether to include observations
+                       show_predictions=False,  # whether to include predictions
+                       show_rowcols=False, # label the rows and columns
+                       frames=False,  # frame counter to show (intended since invocation)
+                       episode=None,  # int episode number to show
+                       step=None,  # int step number to show in image
+                       selected_agent=None,  # indicate which agent is "selected" in the editor
+                       return_image=False # indicate if image is returned for use in monitor:
+                       ):
 
         if type(self.gl) is PILGL:
-            self.gl.beginFrame()
+            self.gl.begin_frame()
 
         env = self.env
 
-        self.renderRail()
+        self.render_rail()
 
         # Draw each agent + its orientation + its target
-        if agents:
-            self.plotAgents(targets=True, iSelectedAgent=iSelectedAgent)
+        if show_agents:
+            self.plot_agents(targets=True, selected_agent=selected_agent)
         if show_observations:
-            self.renderObs(range(env.get_num_agents()), env.dev_obs_dict)
+            self.render_observation(range(env.get_num_agents()), env.dev_obs_dict)
+        if show_predictions and len(env.dev_pred_dict) > 0:
+            self.render_prediction(range(env.get_num_agents()), env.dev_pred_dict)
         # Draw some textual information like fps
-        yText = [-0.3, -0.6, -0.9]
+        text_y = [-0.3, -0.6, -0.9]
         if frames:
-            self.gl.text(0.1, yText[2], "Frame:{:}".format(self.iFrame))
-        self.iFrame += 1
+            self.gl.text(0.1, text_y[2], "Frame:{:}".format(self.frame_nr))
+        self.frame_nr += 1
 
-        if iEpisode is not None:
-            self.gl.text(0.1, yText[1], "Ep:{}".format(iEpisode))
+        if episode is not None:
+            self.gl.text(0.1, text_y[1], "Ep:{}".format(episode))
 
-        if iStep is not None:
-            self.gl.text(0.1, yText[0], "Step:{}".format(iStep))
+        if step is not None:
+            self.gl.text(0.1, text_y[0], "Step:{}".format(step))
 
-        tNow = time.time()
-        self.gl.text(2, yText[2], "elapsed:{:.2f}s".format(tNow - self.time1))
-        self.lTimes.append(tNow)
-        if len(self.lTimes) > 20:
-            self.lTimes.popleft()
-        if len(self.lTimes) > 1:
-            rFps = (len(self.lTimes) - 1) / (self.lTimes[-1] - self.lTimes[0])
-            self.gl.text(2, yText[1], "fps:{:.2f}".format(rFps))
+        time_now = time.time()
+        self.gl.text(2, text_y[2], "elapsed:{:.2f}s".format(time_now - self.start_time))
+        self.times_list.append(time_now)
+        if len(self.times_list) > 20:
+            self.times_list.popleft()
+        if len(self.times_list) > 1:
+            rFps = (len(self.times_list) - 1) / (self.times_list[-1] - self.times_list[0])
+            self.gl.text(2, text_y[1], "fps:{:.2f}".format(rFps))
 
-        self.gl.prettify2(env.width, env.height, self.nPixCell)
+        self.gl.prettify2(env.width, env.height, self.pix_per_cell)
 
         # TODO: for MPL, we don't want to call clf (called by endframe)
         # if not show:
@@ -586,137 +616,155 @@ class RenderTool(object):
 
         self.gl.pause(0.00001)
 
+        if return_image:
+            return self.get_image()
         return
 
-    def _draw_square(self, center, size, color, opacity=255, layer=0):
-        x0 = center[0] - size / 2
-        x1 = center[0] + size / 2
-        y0 = center[1] - size / 2
-        y1 = center[1] + size / 2
-        self.gl.plot([x0, x1, x1, x0, x0], [y0, y0, y1, y1, y0], color=color, layer=layer, opacity=opacity)
-
-    def getImage(self):
-        return self.gl.getImage()
-
-    def plotTreeObs(self, gObs):
-        nBranchFactor = 4
-
-        gP0 = array([[0, 0, 0]]).T
-        nDepth = 2
-        for i in range(nDepth):
-            nDepthNodes = nBranchFactor ** i
-            rShrinkDepth = 1 / (i + 1)
-
-            gX1 = np.linspace(-(nDepthNodes - 1), (nDepthNodes - 1), nDepthNodes) * rShrinkDepth
-            gY1 = np.ones((nDepthNodes)) * i
-            gZ1 = np.zeros((nDepthNodes))
-
-            gP1 = array([gX1, gY1, gZ1])
-            gP01 = np.append(gP0, gP1, axis=1)
-
-            if nDepthNodes > 1:
-                nDepthNodesPrev = nDepthNodes / nBranchFactor
-                giP0 = np.repeat(np.arange(nDepthNodesPrev), nBranchFactor)
-                giP1 = np.arange(0, nDepthNodes) + nDepthNodesPrev
-                giLinePoints = np.stack([giP0, giP1]).ravel("F")
-                self.gl.plot(gP01[0], -gP01[1], lines=giLinePoints, color="gray")
-
-            gP0 = array([gX1, gY1, gZ1])
-
-    def renderEnv2(
-        self, show=False, curves=True, spacing=False, arrows=False, agents=True,
-        show_observations=True, sRailColor="gray",
-        frames=False, iEpisode=None, iStep=None, iSelectedAgent=None,
-        action_dict=dict()
+    def render_env_svg(
+        self, show=False, show_observations=True, show_predictions=False, selected_agent=None,
+        show_agents=True, show_inactive_agents=False, show_rowcols=False, return_image=False
     ):
         """
-        Draw the environment using matplotlib.
-        Draw into the figure if provided.
-
-        Call pyplot.show() if show==True.
-        (Use show=False from a Jupyter notebook with %matplotlib inline)
+        Renders the environment with SVG support (nice image)
         """
 
         env = self.env
 
-        self.gl.beginFrame()
+        self.gl.begin_frame()
 
         if self.new_rail:
             self.new_rail = False
             self.gl.clear_rails()
 
             # store the targets
-            dTargets = {}
-            dSelected = {}
-            for iAgent, agent in enumerate(self.env.agents_static):
+            targets = {}
+            selected = {}
+            for agent_idx, agent in enumerate(self.env.agents):
                 if agent is None:
                     continue
-                dTargets[tuple(agent.target)] = iAgent
-                dSelected[tuple(agent.target)] = (iAgent == iSelectedAgent)
+                targets[tuple(agent.target)] = agent_idx
+                selected[tuple(agent.target)] = (agent_idx == selected_agent)
 
             # Draw each cell independently
             for r in range(env.height):
                 for c in range(env.width):
-                    binTrans = env.rail.grid[r, c]
-                    if (r, c) in dTargets:
-                        target = dTargets[(r, c)]
-                        isSelected = dSelected[(r, c)]
+                    transitions = env.rail.grid[r, c]
+                    if (r, c) in targets:
+                        target = targets[(r, c)]
+                        is_selected = selected[(r, c)]
                     else:
                         target = None
-                        isSelected = False
+                        is_selected = False
 
-                    self.gl.setRailAt(r, c, binTrans, iTarget=target, isSelected=isSelected, rail_grid=env.rail.grid)
+                    self.gl.set_rail_at(r, c, transitions, target=target, is_selected=is_selected,
+                                        rail_grid=env.rail.grid, num_agents=env.get_num_agents(),
+                                        show_debug=self.show_debug)
 
-            self.gl.build_background_map(dTargets)
+            self.gl.build_background_map(targets)
 
-        for iAgent, agent in enumerate(self.env.agents):
+            if show_rowcols:
+                # label rows, cols
+                for iRow in range(env.height):
+                    self.gl.text_rowcol((iRow, 0), str(iRow), layer=self.gl.RAIL_LAYER)
+                for iCol in range(env.width):
+                    self.gl.text_rowcol((0, iCol), str(iCol), layer=self.gl.RAIL_LAYER)
 
-            if agent is None:
-                continue
 
-            if self.agentRenderVariant == AgentRenderVariant.BOX_ONLY:
-                self.gl.setCellOccupied(iAgent, *(agent.position))
-            elif self.agentRenderVariant == AgentRenderVariant.ONE_STEP_BEHIND or \
-                self.agentRenderVariant == AgentRenderVariant.ONE_STEP_BEHIND_AND_BOX:  # noqa: E125
-                if agent.old_position is not None:
-                    position = agent.old_position
-                    direction = agent.direction
-                    old_direction = agent.old_direction
+        if show_agents:
+            for agent_idx, agent in enumerate(self.env.agents):
+
+                if agent is None:
+                    continue
+
+                # Show an agent even if it hasn't already started
+                if agent.position is None:
+                    if show_inactive_agents:
+                        # print("agent ", agent_idx, agent.position, agent.old_position, agent.initial_position)
+                        self.gl.set_agent_at(agent_idx, *(agent.initial_position), 
+                            agent.initial_direction, agent.initial_direction,
+                            is_selected=(selected_agent == agent_idx),
+                            rail_grid=env.rail.grid,
+                            show_debug=self.show_debug, clear_debug_text=self.clear_debug_text,
+                            malfunction=False)
+                    continue
+
+                is_malfunction = agent.malfunction_handler.malfunction_down_counter > 0
+
+                if self.agent_render_variant == AgentRenderVariant.BOX_ONLY:
+                    self.gl.set_cell_occupied(agent_idx, *(agent.position))
+
+                elif self.agent_render_variant == AgentRenderVariant.ONE_STEP_BEHIND or \
+                    self.agent_render_variant == AgentRenderVariant.ONE_STEP_BEHIND_AND_BOX:  # noqa: E125
+
+                    # Most common case - the agent has been running for >1 steps
+                    if agent.old_position is not None:
+                        position = agent.old_position
+                        direction = agent.direction
+                        old_direction = agent.old_direction
+
+                    # the agent's first step - it doesn't have an old position yet
+                    elif agent.position is not None:
+                        position = agent.position
+                        direction = agent.direction
+                        old_direction = agent.direction
+                        
+                    # When the editor has just added an agent
+                    elif agent.initial_position is not None:
+                        position = agent.initial_position
+                        direction = agent.initial_direction
+                        old_direction = agent.initial_direction
+
+                    # set_agent_at uses the agent index for the color
+                    if self.agent_render_variant == AgentRenderVariant.ONE_STEP_BEHIND_AND_BOX:
+                        self.gl.set_cell_occupied(agent_idx, *(agent.position))
+                    self.gl.set_agent_at(agent_idx, *position, old_direction, direction,
+                                         selected_agent == agent_idx, rail_grid=env.rail.grid,
+                                         show_debug=self.show_debug, clear_debug_text=self.clear_debug_text,
+                                         malfunction=is_malfunction)
                 else:
                     position = agent.position
                     direction = agent.direction
-                    old_direction = agent.direction
+                    for possible_direction in range(4):
+                        # Is a transition along movement `desired_movement_from_new_cell` to the current cell possible?
+                        isValid = env.rail.get_transition((*agent.position, agent.direction), possible_direction)
+                        if isValid:
+                            direction = possible_direction
+
+                            # set_agent_at uses the agent index for the color
+                            self.gl.set_agent_at(agent_idx, *position, agent.direction, direction,
+                                                 selected_agent == agent_idx, rail_grid=env.rail.grid,
+                                                 show_debug=self.show_debug, clear_debug_text=self.clear_debug_text,
+                                                 malfunction=is_malfunction)
+
+                    # set_agent_at uses the agent index for the color
+                    if self.agent_render_variant == AgentRenderVariant.AGENT_SHOWS_OPTIONS_AND_BOX:
+                        self.gl.set_cell_occupied(agent_idx, *(agent.position))
+                    
+                    if show_inactive_agents:
+                        show_this_agent = True
+                    else:
+                        show_this_agent = agent.state.is_on_map_state()
 
-                # setAgentAt uses the agent index for the color
-                if self.agentRenderVariant == AgentRenderVariant.ONE_STEP_BEHIND_AND_BOX:
-                    self.gl.setCellOccupied(iAgent, *(agent.position))
-                self.gl.setAgentAt(iAgent, *position, old_direction, direction, iSelectedAgent == iAgent)
-            else:
-                position = agent.position
-                direction = agent.direction
-                for possible_directions in range(4):
-                    # Is a transition along movement `desired_movement_from_new_cell' to the current cell possible?
-                    isValid = env.rail.get_transition((*agent.position, agent.direction), possible_directions)
-                    if isValid:
-                        direction = possible_directions
-
-                        # setAgentAt uses the agent index for the color
-                        self.gl.setAgentAt(iAgent, *position, agent.direction, direction, iSelectedAgent == iAgent)
-
-                # setAgentAt uses the agent index for the color
-                if self.agentRenderVariant == AgentRenderVariant.AGENT_SHOWS_OPTIONS_AND_BOX:
-                    self.gl.setCellOccupied(iAgent, *(agent.position))
-                self.gl.setAgentAt(iAgent, *position, agent.direction, direction, iSelectedAgent == iAgent)
+                    if show_this_agent:
+                        self.gl.set_agent_at(agent_idx, *position, agent.direction, direction, 
+                                        selected_agent == agent_idx,
+                                        rail_grid=env.rail.grid, malfunction=is_malfunction)
 
         if show_observations:
-            self.renderObs(range(env.get_num_agents()), env.dev_obs_dict)
+            self.render_observation(range(env.get_num_agents()), env.dev_obs_dict)
+        if show_predictions:
+            self.render_prediction(range(env.get_num_agents()), env.dev_pred_dict)
+        
+
 
         if show:
             self.gl.show()
         for i in range(3):
-            self.gl.processEvents()
+            self.gl.process_events()
 
-        self.iFrame += 1
+        self.frame_nr += 1
+        if return_image:
+            return self.get_image()
         return
 
     def close_window(self):

flatland/utils/simple_rail.py

-from typing import Tuple
+from typing import Tuple, Dict
 
 import numpy as np
 
-from flatland.core.grid.grid4 import Grid4Transitions
+from flatland.core.grid.rail_env_grid import RailEnvTransitions
 from flatland.core.transition_map import GridTransitionMap
 
 
 def make_simple_rail() -> Tuple[GridTransitionMap, np.array]:
+    # We instantiate a very simple rail network on a 7x10 grid:
+    # Note that that cells have invalid RailEnvTransitions!
+    #        |
+    #        |
+    #        |
+    #    _ _ _ _\ _ _  _  _ _ _
+    #                /
+    #                |
+    #                |
+    #                |
+    transitions = RailEnvTransitions()
+    cells = transitions.transition_list
+    empty = cells[0]
+    dead_end_from_south = cells[7]
+    dead_end_from_west = transitions.rotate_transition(dead_end_from_south, 90)
+    dead_end_from_north = transitions.rotate_transition(dead_end_from_south, 180)
+    dead_end_from_east = transitions.rotate_transition(dead_end_from_south, 270)
+    vertical_straight = cells[1]
+    horizontal_straight = transitions.rotate_transition(vertical_straight, 90)
+    simple_switch_north_left = cells[2]
+    simple_switch_north_right = cells[10]
+    simple_switch_east_west_north = transitions.rotate_transition(simple_switch_north_right, 270)
+    simple_switch_east_west_south = transitions.rotate_transition(simple_switch_north_left, 270)
+    rail_map = np.array(
+        [[empty] * 3 + [dead_end_from_south] + [empty] * 6] +
+        [[empty] * 3 + [vertical_straight] + [empty] * 6] * 2 +
+        [[dead_end_from_east] + [horizontal_straight] * 2 +
+         [simple_switch_east_west_north] +
+         [horizontal_straight] * 2 + [simple_switch_east_west_south] +
+         [horizontal_straight] * 2 + [dead_end_from_west]] +
+        [[empty] * 6 + [vertical_straight] + [empty] * 3] * 2 +
+        [[empty] * 6 + [dead_end_from_north] + [empty] * 3], dtype=np.uint16)
+    rail = GridTransitionMap(width=rail_map.shape[1],
+                             height=rail_map.shape[0], transitions=transitions)
+    rail.grid = rail_map
+    city_positions = [(0,3), (6, 6)]
+    train_stations = [
+                      [( (0, 3), 0 ) ],
+                      [( (6, 6), 0 ) ],
+                     ]
+    city_orientations = [0, 2]
+    agents_hints = {'city_positions': city_positions,
+                    'train_stations': train_stations,
+                    'city_orientations': city_orientations
+                   }
+    optionals = {'agents_hints': agents_hints}
+    return rail, rail_map, optionals
+
+
+def make_disconnected_simple_rail() -> Tuple[GridTransitionMap, np.array]:
+    # We instantiate a very simple rail network on a 7x10 grid:
+    # Note that that cells have invalid RailEnvTransitions!
+    #        |
+    #        |
+    #        |
+    # _ _ _ _\ _    _  _ _ _
+    #                /
+    #                |
+    #                |
+    #                |
+
+    transitions = RailEnvTransitions()
+    cells = transitions.transition_list
+    empty = cells[0]
+    dead_end_from_south = cells[7]
+    dead_end_from_west = transitions.rotate_transition(dead_end_from_south, 90)
+    dead_end_from_north = transitions.rotate_transition(dead_end_from_south, 180)
+    dead_end_from_east = transitions.rotate_transition(dead_end_from_south, 270)
+    vertical_straight = cells[1]
+    horizontal_straight = transitions.rotate_transition(vertical_straight, 90)
+    simple_switch_north_left = cells[2]
+    simple_switch_north_right = cells[10]
+    simple_switch_east_west_north = transitions.rotate_transition(simple_switch_north_right, 270)
+    simple_switch_east_west_south = transitions.rotate_transition(simple_switch_north_left, 270)
+    rail_map = np.array(
+        [[empty] * 3 + [dead_end_from_south] + [empty] * 6] +
+        [[empty] * 3 + [vertical_straight] + [empty] * 6] * 2 +
+        [[dead_end_from_east] + [horizontal_straight] * 2 +
+         [simple_switch_east_west_north] +
+         [dead_end_from_west] + [dead_end_from_east] + [simple_switch_east_west_south] +
+         [horizontal_straight] * 2 + [dead_end_from_west]] +
+        [[empty] * 6 + [vertical_straight] + [empty] * 3] * 2 +
+        [[empty] * 6 + [dead_end_from_north] + [empty] * 3], dtype=np.uint16)
+    rail = GridTransitionMap(width=rail_map.shape[1],
+                             height=rail_map.shape[0], transitions=transitions)
+    rail.grid = rail_map
+    city_positions = [(0,3), (6, 6)]
+    train_stations = [
+                      [( (0, 3), 0 ) ],
+                      [( (6, 6), 0 ) ],
+                     ]
+    city_orientations = [0, 2]
+    agents_hints = {'city_positions': city_positions,
+                    'train_stations': train_stations,
+                    'city_orientations': city_orientations
+                   }
+    optionals = {'agents_hints': agents_hints}
+    return rail, rail_map, optionals
+
+
+def make_simple_rail2() -> Tuple[GridTransitionMap, np.array]:
+    # We instantiate a very simple rail network on a 7x10 grid:
+    #        |
+    #        |
+    #        |
+    # _ _ _ _\ _ _  _  _ _ _
+    #               \
+    #                |
+    #                |
+    #                |
+    transitions = RailEnvTransitions()
+    cells = transitions.transition_list
+    empty = cells[0]
+    dead_end_from_south = cells[7]
+    dead_end_from_west = transitions.rotate_transition(dead_end_from_south, 90)
+    dead_end_from_north = transitions.rotate_transition(dead_end_from_south, 180)
+    dead_end_from_east = transitions.rotate_transition(dead_end_from_south, 270)
+    vertical_straight = cells[1]
+    horizontal_straight = transitions.rotate_transition(vertical_straight, 90)
+    simple_switch_north_right = cells[10]
+    simple_switch_east_west_north = transitions.rotate_transition(simple_switch_north_right, 270)
+    simple_switch_west_east_south = transitions.rotate_transition(simple_switch_north_right, 90)
+    rail_map = np.array(
+        [[empty] * 3 + [dead_end_from_south] + [empty] * 6] +
+        [[empty] * 3 + [vertical_straight] + [empty] * 6] * 2 +
+        [[dead_end_from_east] + [horizontal_straight] * 2 +
+         [simple_switch_east_west_north] +
+         [horizontal_straight] * 2 + [simple_switch_west_east_south] +
+         [horizontal_straight] * 2 + [dead_end_from_west]] +
+        [[empty] * 6 + [vertical_straight] + [empty] * 3] * 2 +
+        [[empty] * 6 + [dead_end_from_north] + [empty] * 3], dtype=np.uint16)
+    rail = GridTransitionMap(width=rail_map.shape[1],
+                             height=rail_map.shape[0], transitions=transitions)
+    rail.grid = rail_map
+    city_positions = [(0,3), (6, 6)]
+    train_stations = [
+                      [( (0, 3), 0 ) ],
+                      [( (6, 6), 0 ) ],
+                     ]
+    city_orientations = [0, 2]
+    agents_hints = {'city_positions': city_positions,
+                    'train_stations': train_stations,
+                    'city_orientations': city_orientations
+                   }
+    optionals = {'agents_hints': agents_hints}
+    return rail, rail_map, optionals
+
+
+def make_simple_rail_unconnected() -> Tuple[GridTransitionMap, np.array]:
+    # We instantiate a very simple rail network on a 7x10 grid:
+    # Note that that cells have invalid RailEnvTransitions!
+    #        |
+    #        |
+    #        |
+    # _ _ _  _ _ _  _  _ _ _
+    #                /
+    #                |
+    #                |
+    #                |
+    transitions = RailEnvTransitions()
+    cells = transitions.transition_list
+    empty = cells[0]
+    dead_end_from_south = cells[7]
+    dead_end_from_west = transitions.rotate_transition(dead_end_from_south, 90)
+    dead_end_from_north = transitions.rotate_transition(dead_end_from_south, 180)
+    dead_end_from_east = transitions.rotate_transition(dead_end_from_south, 270)
+    vertical_straight = cells[1]
+    horizontal_straight = transitions.rotate_transition(vertical_straight, 90)
+    simple_switch_north_left = cells[2]
+    # simple_switch_north_right = cells[10]
+    # simple_switch_east_west_north = transitions.rotate_transition(simple_switch_north_right, 270)
+    simple_switch_east_west_south = transitions.rotate_transition(simple_switch_north_left, 270)
+    rail_map = np.array(
+        [[empty] * 3 + [dead_end_from_south] + [empty] * 6] +
+        [[empty] * 3 + [vertical_straight] + [empty] * 6] +
+        [[empty] * 3 + [dead_end_from_north] + [empty] * 6] +
+        [[dead_end_from_east] + [horizontal_straight] * 5 + [simple_switch_east_west_south] +
+         [horizontal_straight] * 2 + [dead_end_from_west]] +
+        [[empty] * 6 + [vertical_straight] + [empty] * 3] * 2 +
+        [[empty] * 6 + [dead_end_from_north] + [empty] * 3], dtype=np.uint16)
+    rail = GridTransitionMap(width=rail_map.shape[1],
+                             height=rail_map.shape[0], transitions=transitions)
+    rail.grid = rail_map
+    city_positions = [(0,3), (6, 6)]
+    train_stations = [
+                      [( (0, 3), 0 ) ],
+                      [( (6, 6), 0 ) ],
+                     ]
+    city_orientations = [0, 2]
+    agents_hints = {'city_positions': city_positions,
+                    'train_stations': train_stations,
+                    'city_orientations': city_orientations
+                   }
+    optionals = {'agents_hints': agents_hints}
+    return rail, rail_map, optionals
+
+
+def make_simple_rail_with_alternatives() -> Tuple[GridTransitionMap, np.array]:
+    # We instantiate a very simple rail network on a 7x10 grid:
+    #  0 1 2 3 4 5 6 7 8 9  10
+    # 0        /-------------\
+    # 1        |             |
+    # 2        |             |
+    # 3 _ _ _ /_  _ _        |
+    # 4              \   ___ /
+    # 5               |/
+    # 6               |
+    # 7               |
+    transitions = RailEnvTransitions()
+    cells = transitions.transition_list
+
+    empty = cells[0]
+    dead_end_from_south = cells[7]
+    right_turn_from_south = cells[8]
+    right_turn_from_west = transitions.rotate_transition(right_turn_from_south, 90)
+    right_turn_from_north = transitions.rotate_transition(right_turn_from_south, 180)
+    dead_end_from_west = transitions.rotate_transition(dead_end_from_south, 90)
+    dead_end_from_north = transitions.rotate_transition(dead_end_from_south, 180)
+    dead_end_from_east = transitions.rotate_transition(dead_end_from_south, 270)
+    vertical_straight = cells[1]
+    simple_switch_north_left = cells[2]
+    simple_switch_north_right = cells[10]
+    simple_switch_left_east = transitions.rotate_transition(simple_switch_north_left, 90)
+    horizontal_straight = transitions.rotate_transition(vertical_straight, 90)
+    double_switch_south_horizontal_straight = horizontal_straight + cells[6]
+    double_switch_north_horizontal_straight = transitions.rotate_transition(
+        double_switch_south_horizontal_straight, 180)
+    rail_map = np.array(
+        [[empty] * 3 + [right_turn_from_south] + [horizontal_straight] * 5 + [right_turn_from_west]] +
+        [[empty] * 3 + [vertical_straight] + [empty] * 5 + [vertical_straight]] * 2 +
+        [[dead_end_from_east] + [horizontal_straight] * 2 + [simple_switch_left_east] + [horizontal_straight] * 2 + [
+            right_turn_from_west] + [empty] * 2 + [vertical_straight]] +
+        [[empty] * 6 + [simple_switch_north_right] + [horizontal_straight] * 2 + [right_turn_from_north]] +
+        [[empty] * 6 + [vertical_straight] + [empty] * 3] +
+        [[empty] * 6 + [dead_end_from_north] + [empty] * 3], dtype=np.uint16)
+    rail = GridTransitionMap(width=rail_map.shape[1],
+                             height=rail_map.shape[0], transitions=transitions)
+    rail.grid = rail_map
+    city_positions = [(0,3), (6, 6)]
+    train_stations = [
+                      [( (0, 3), 0 ) ],
+                      [( (6, 6), 0 ) ],
+                     ]
+    city_orientations = [0, 2]
+    agents_hints = {'city_positions': city_positions,
+                    'train_stations': train_stations,
+                    'city_orientations': city_orientations
+                   }
+    optionals = {'agents_hints': agents_hints}
+    return rail, rail_map, optionals
+
+
+
+def make_invalid_simple_rail() -> Tuple[GridTransitionMap, np.array, Dict[str, str]]:
     # We instantiate a very simple rail network on a 7x10 grid:
     #        |
     #        |
@@ -16,15 +270,9 @@ def make_simple_rail() -> Tuple[GridTransitionMap, np.array]:
     #                |
     #                |
     #                |
-    cells = [int('0000000000000000', 2),  # empty cell - Case 0
-             int('1000000000100000', 2),  # Case 1 - straight
-             int('1001001000100000', 2),  # Case 2 - simple switch
-             int('1000010000100001', 2),  # Case 3 - diamond drossing
-             int('1001011000100001', 2),  # Case 4 - single slip switch
-             int('1100110000110011', 2),  # Case 5 - double slip switch
-             int('0101001000000010', 2),  # Case 6 - symmetrical switch
-             int('0010000000000000', 2)]  # Case 7 - dead end
-    transitions = Grid4Transitions([])
+    transitions = RailEnvTransitions()
+    cells = transitions.transition_list
+
     empty = cells[0]
     dead_end_from_south = cells[7]
     dead_end_from_west = transitions.rotate_transition(dead_end_from_south, 90)
@@ -47,4 +295,51 @@ def make_simple_rail() -> Tuple[GridTransitionMap, np.array]:
     rail = GridTransitionMap(width=rail_map.shape[1],
                              height=rail_map.shape[0], transitions=transitions)
     rail.grid = rail_map
-    return rail, rail_map
+    city_positions = [(0,3), (6, 6)]
+    train_stations = [
+                      [( (0, 3), 0 ) ],
+                      [( (6, 6), 0 ) ],
+                     ]
+    city_orientations = [0, 2]
+    agents_hints = {'city_positions': city_positions,
+                    'train_stations': train_stations,
+                    'city_orientations': city_orientations
+                   }
+    optionals = {'agents_hints': agents_hints}
+    return rail, rail_map, optionals
+
+def make_oval_rail() -> Tuple[GridTransitionMap, np.array]:
+    transitions = RailEnvTransitions()
+    cells = transitions.transition_list
+
+    empty = cells[0]
+    vertical_straight = cells[1]
+    horizontal_straight = transitions.rotate_transition(vertical_straight, 90)
+    right_turn_from_south = cells[8]
+    right_turn_from_west = transitions.rotate_transition(right_turn_from_south, 90)
+    right_turn_from_north = transitions.rotate_transition(right_turn_from_south, 180)
+    right_turn_from_east = transitions.rotate_transition(right_turn_from_south, 270)
+
+    rail_map = np.array(
+        [[empty] * 9] +
+        [[empty] + [right_turn_from_south] + [horizontal_straight] * 5 + [right_turn_from_west] + [empty]] +
+        [[empty] + [vertical_straight] + [empty] * 5 + [vertical_straight] + [empty]]+
+        [[empty] + [vertical_straight] + [empty] * 5 + [vertical_straight] + [empty]] +
+        [[empty] + [right_turn_from_east] + [horizontal_straight] * 5 + [right_turn_from_north] + [empty]] +
+        [[empty] * 9], dtype=np.uint16)
+
+    rail = GridTransitionMap(width=rail_map.shape[1],
+                             height=rail_map.shape[0], transitions=transitions)
+    rail.grid = rail_map
+    city_positions = [(1, 4), (4, 4)]
+    train_stations = [
+        [((1, 4), 0)],
+        [((4, 4), 0)],
+    ]
+    city_orientations = [1, 3]
+    agents_hints = {'city_positions': city_positions,
+                    'train_stations': train_stations,
+                    'city_orientations': city_orientations
+                    }
+    optionals = {'agents_hints': agents_hints}
+    return  rail, rail_map, optionals

flatland_2.0.md

+Flatland 2.0 Introduction
+=========================
+
+## What's new?
+
+In this version of **Flat**land, we are moving closer to realistic and more complex railway problems. 
+Earlier versions of **Flat**land introduced you to the concept of restricted transitions, but they were still too simplistic to give us feasible solutions for daily operations. 
+Thus the following changes are coming in the next version to be closer to real railway network challenges:
+
+- **New Level Generator** provide less connections between different nodes in the network and thus agent densities on rails are much higher.
+- **Stochastic Events** cause agents to stop and get stuck for different numbers of time steps.
+- **Different Speed Classes** allow agents to move at different speeds and thus enhance complexity in the search for optimal solutions.
+
+
+We explain these changes in more detail and how you can play with their parametrization in Tutorials 3--5:
+* [Tutorials](https://gitlab.aicrowd.com/flatland/flatland/tree/master/docs/tutorials)
+
+We appreciate *your feedback* on the performance and the difficulty on these levels to help us shape the best possible **Flat**land 2.0 environment.
+
+## Example code
+
+To see all the changes in action you can just run the 
+* [examples/flatland_example_2_0.py](https://gitlab.aicrowd.com/flatland/flatland/blob/master/examples/flatland_2_0_example.py) 
+
+example.

getting_started/getting_started.sh

@@ -3,7 +3,7 @@ set -e # stop on error
 set -x # echo commands
 
 
-FLATLAND_BASEDIR=$(dirname "$0")/..
+FLATLAND_BASEDIR=$(dirname "$BASH_SOURCE")/..
 cd ${FLATLAND_BASEDIR}
 
 conda install -y -c conda-forge tox-conda

make_docs.py

 #!/usr/bin/env python
-
+import glob
 import os
+import shutil
 import subprocess
 import webbrowser
 from urllib.request import pathname2url
@@ -18,16 +19,43 @@ def remove_exists(filename):
 
 
 # clean docs config and html files, and rebuild everything
-remove_exists('docs/flatland.rst')
+# wildcards do not work under Windows
+for image_file in glob.glob(r'./docs/flatland*.rst'):
+    remove_exists(image_file)
 remove_exists('docs/modules.rst')
 
-subprocess.call(['sphinx-apidoc', '-o', 'docs/', 'flatland'])
+for md_file in glob.glob(r'./*.md') + glob.glob(r'./docs/specifications/*.md') + glob.glob(r'./docs/tutorials/*.md') + glob.glob(r'./docs/interface/*.md'):
+    from m2r import parse_from_file
+
+    rst_content = parse_from_file(md_file)
+    rst_file = md_file.replace(".md", ".rst")
+    remove_exists(rst_file)
+    with open(rst_file, 'w') as out:
+        print("m2r {}->{}".format(md_file, rst_file))
+
+        out.write(rst_content)
+        out.flush()
+
+img_dest = 'docs/images/'
+if not os.path.exists(img_dest):
+    os.makedirs(img_dest)
+for image_file in glob.glob(r'./images/*.png'):
+    shutil.copy(image_file, img_dest)
+
+subprocess.call(['sphinx-apidoc', '--force', '-a', '-e', '-o', 'docs/', 'flatland', '-H', 'API Reference', '--tocfile',
+                 '05_apidoc'])
 
-os.environ["SPHINXPROJ"] = "flatland"
+os.environ["SPHINXPROJ"] = "Flatland"
 os.chdir('docs')
 subprocess.call(['python', '-msphinx', '-M', 'clean', '.', '_build'])
-# TODO fix sphinx warnings instead of suppressing them...
-subprocess.call(['python', '-msphinx', '-M', 'html', '.', '_build', '-Q'])
-subprocess.call(['python', '-mpydeps', '../flatland', '-o', '_build/html/flatland.svg', '--no-config', '--noshow'])
+img_dest = '_build/html/img'
+if not os.path.exists(img_dest):
+    os.makedirs(img_dest)
+for image_file in glob.glob(r'./specifications/img/*'):
+    shutil.copy(image_file, img_dest)
+subprocess.call(['python', '-msphinx', '-M', 'html', '.', '_build'])
+
+# we do not currrently use pydeps, commented out https://gitlab.aicrowd.com/flatland/flatland/issues/149
+# subprocess.call(['python', '-mpydeps', '../flatland', '-o', '_build/html/flatland.svg', '--no-config', '--noshow'])
 
 browser('_build/html/index.html')

notebooks/flatland_animate.ipynb

+%% Cell type:markdown id: tags:
+
+# Simple Animation Demo
+
+%% Cell type:code id: tags:
+
+``` python
+%load_ext autoreload
+%autoreload 2
+```
+
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+import time
+from IPython import display
+from ipycanvas import canvas
+from flatland.utils.rendertools import RenderTool
+from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_env import RailEnvActions as rea
+from flatland.envs.persistence import RailEnvPersister
+```
+
+%% Cell type:code id: tags:
+
+``` python
+env, env_dict = RailEnvPersister.load_new("complex_scene_2.pkl", load_from_package="env_data.railway")
+_ = env.reset()
+env._max_episode_steps = 100
+```
+
+%% Output
+
+    pickle failed to load file: complex_scene_2.pkl  trying msgpack (deprecated)...
+    pickle failed to load file: complex_scene_2.pkl  trying msgpack (deprecated)...
+    pickle failed to load file: complex_scene_2.pkl  trying msgpack (deprecated)...
+    This env file has no max_episode_steps (deprecated) - setting to 100
+
+%% Cell type:code id: tags:
+
+``` python
+oRT = RenderTool(env, gl="PILSVG", jupyter=False, show_debug=True)
+image_arr = oRT.get_image()
+oCanvas = canvas.Canvas()
+oCanvas.put_image_data(image_arr[:,:,0:3])
+display.display(oCanvas)
+
+done={"__all__":False}
+while not done["__all__"]:
+    actions = {}
+    for agent_handle, agents in enumerate(env.agents):
+        actions.update({agent_handle:rea.MOVE_FORWARD})
+    obs, rew, done, info = env.step(actions)
+    oRT.render_env(show_observations=False,show_predictions=False)
+    gIm = oRT.get_image()
+    oCanvas.put_image_data(gIm[:,:,0:3])
+    time.sleep(0.1)
+```
+
+%% Output
+

notebooks/notebook-list

+# list of notebooks to include in run-all-notebooks.py test
+simple_example_manual_control.ipynb
+simple_rendering_demo.ipynb
+flatland_animate.ipynb
+render_episode.ipynb
+scene_editor.ipynb
+test_saved_envs.ipynb
+test_service.ipynb

notebooks/render_episode.ipynb

+%% Cell type:markdown id: tags:
+
+# Render Episode
+Render a stored episode.  Env file needs to have "episode" and "action" keys.
+- creates a moving gif file of the episode
+- displays the episode in a widget with a slider for the time steps.
+
+%% Cell type:markdown id: tags:
+
+# Setup
+
+%% Cell type:code id: tags:
+
+``` python
+#!apt -qq install graphviz libgraphviz-dev pkg-config
+#!pip install -qq git+https://gitlab.aicrowd.com/flatland/flatland.git
+```
+
+%% Cell type:code id: tags:
+
+``` python
+%load_ext autoreload
+%autoreload 2
+```
+
+%% Cell type:code id: tags:
+
+``` python
+from IPython import display
+```
+
+%% Cell type:code id: tags:
+
+``` python
+import os
+import pandas as pd
+import PIL
+import imageio
+```
+
+%% Cell type:code id: tags:
+
+``` python
+from flatland.utils.rendertools import RenderTool
+from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_generators import sparse_rail_generator
+from flatland.envs.line_generators import sparse_line_generator
+from flatland.envs.malfunction_generators import malfunction_from_file, no_malfunction_generator
+from flatland.envs.rail_generators import rail_from_file
+from flatland.envs.rail_env import RailEnvActions
+from flatland.envs.step_utils.states import TrainState
+from flatland.envs.persistence import RailEnvPersister
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def render_env(env_renderer):
+    ag0= env_renderer.env.agents[0]
+    #print("render_env ag0: ",ag0.position, ag0.direction)
+    aImage = env_renderer.render_env(show_rowcols=True, return_image=True)
+    pil_image = PIL.Image.fromarray(aImage)
+    return pil_image
+```
+
+%% Cell type:markdown id: tags:
+
+# Experiments
+
+This has been mostly changed to load envs using `importlib_resources`.  It's getting them from the package "envdata.tests`
+
+%% Cell type:code id: tags:
+
+``` python
+env, env_dict = RailEnvPersister.load_new("complex_scene_2.pkl", load_from_package="env_data.railway")
+_ = env.reset()
+env._max_episode_steps = 100
+```
+
+%% Output
+
+    pickle failed to load file: complex_scene_2.pkl  trying msgpack (deprecated)...
+    pickle failed to load file: complex_scene_2.pkl  trying msgpack (deprecated)...
+    pickle failed to load file: complex_scene_2.pkl  trying msgpack (deprecated)...
+    This env file has no max_episode_steps (deprecated) - setting to 100
+
+%% Cell type:code id: tags:
+
+``` python
+# the seed has to match that used to record the episode, in order for the malfunctions to match.
+oRT = RenderTool(env, show_debug=True)
+aImg = oRT.render_env(show_rowcols=True, return_image=True, show_inactive_agents=True)
+print(env._max_episode_steps)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+loAgs = env_dict["agents"]
+lCols =  "initial_direction,direction,initial_position,position".split(",")
+pd.DataFrame([ [getattr(oAg, sCol) for sCol in lCols]
+              for oAg in loAgs], columns=lCols)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+pd.DataFrame([ [getattr(oAg, sCol) for sCol in lCols]
+              for oAg in env.agents], columns=lCols)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+pd.DataFrame([ vars(oAg) for oAg in env.agents])
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# from persistence.py
+def get_agent_state(env):
+    list_agents_state = []
+    for iAg, oAg in enumerate(env.agents):
+        # the int cast is to avoid numpy types which may cause problems with msgpack
+        # in env v2, agents may have position None, before starting
+        if oAg.position is None:
+            pos = (0, 0)
+        else:
+            pos = (int(oAg.position[0]), int(oAg.position[1]))
+        # print("pos:", pos, type(pos[0]))
+        list_agents_state.append(
+            [*pos, int(oAg.direction), oAg.malfunction_handler])
+
+    return list_agents_state
+```
+
+%% Cell type:code id: tags:
+
+``` python
+pd.DataFrame([ vars(oAg) for oAg in env.agents])
+```
+
+%% Cell type:code id: tags:
+
+``` python
+expert_actions = []
+action = {}
+```
+
+%% Cell type:code id: tags:
+
+``` python
+env_renderer = RenderTool(env, gl="PGL", show_debug=True)
+
+n_agents = env.get_num_agents()
+x_dim, y_dim = env.width, env.height
+max_steps = env._max_episode_steps
+
+action_dict = {}
+frames = []
+
+# log everything in original state
+statuses = []
+for a in range(n_agents):
+    statuses.append(env.agents[a].state)
+pilImg = render_env(env_renderer)
+frames.append({
+    'image': pilImg,
+    'statuses': statuses
+    })
+
+step = 0
+all_done = False
+failed_action_check = False
+print("Processing episode steps:")
+while not all_done:
+    print(step, end=", ")
+
+    for agent_handle, agent in enumerate(env.agents):
+        action_dict.update({agent_handle: RailEnvActions.MOVE_FORWARD})
+
+    next_obs, all_rewards, done, info = env.step(action_dict)
+
+    statuses = []
+    for a in range(n_agents):
+        statuses.append(env.agents[a].state)
+
+    #clear_output(wait=True)
+    pilImg = render_env(env_renderer)
+    frames.append({
+        'image': pilImg,
+        'statuses': statuses
+      })
+    #print("Replaying {}/{}".format(step, max_steps))
+
+    if done['__all__']:
+        all_done = True
+        max_steps = step + 1
+        print("done")
+
+    step += 1
+```
+
+%% Cell type:code id: tags:
+
+``` python
+assert failed_action_check == False, "Realised states did not match stored states."
+```
+
+%% Cell type:code id: tags:
+
+``` python
+from ipywidgets import interact, interactive, fixed, interact_manual, Play
+import ipywidgets as widgets
+import matplotlib.pyplot as plt
+%matplotlib inline
+
+import numpy as np
+from IPython.display import HTML
+
+display.display(HTML('<link rel="stylesheet" href="//stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css"/>'))
+
+def plot_func(frame_idx):
+    frame = frames[int(frame_idx)]
+    display.display(frame['image'])
+    #print(frame['statuses'])
+
+
+    slider = widgets.FloatSlider(value=0, min=0, max=max_steps, step=1)
+    interact(plot_func, frame_idx = slider)
+
+    play = Play(
+        max=max_steps,
+        value=0,
+        step=1,
+        interval=250
+    )
+
+    widgets.link((play, 'value'), (slider, 'value'))
+    widgets.VBox([play])
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```

notebooks/run_all_notebooks.py

@@ -5,6 +5,7 @@ from subprocess import Popen, PIPE
 import importlib_resources
 import pkg_resources
 from importlib_resources import path
+import importlib_resources as ir
 from ipython_genutils.py3compat import string_types, bytes_to_str
 
 
@@ -38,17 +39,38 @@ def run_python(parameters, ignore_return_code=False, stdin=None):
     return stdout.decode('utf8', 'replace'), stderr.decode('utf8', 'replace')
 
 
-for entry in [entry for entry in importlib_resources.contents('notebooks') if
-              not pkg_resources.resource_isdir('notebooks', entry)
-              and entry.endswith(".ipynb")
-              ]:
-    print("*****************************************************************")
-    print("Converting and running {}".format(entry))
-    print("*****************************************************************")
-
-    with path('notebooks', entry) as file_in:
-        out, err = run_python(" -m jupyter nbconvert --execute --to notebook --inplace " + str(file_in))
-        sys.stderr.write(err)
-        sys.stderr.flush()
-        sys.stdout.write(out)
-        sys.stdout.flush()
+def main():
+
+    # If the file notebooks-list exists, use it as a definitive list of notebooks to run
+    # This in effect ignores any local notebooks you might be working on, so you can run tox
+    # without them causing the notebooks task / testenv to fail.
+    if importlib_resources.is_resource("notebooks", "notebook-list"):
+        print("Using the notebooks-list file to designate which notebooks to run")
+        lsNB = [
+            sLine for sLine in ir.read_text("notebooks", "notebook-list").split("\n") 
+            if len(sLine) > 3 and not sLine.startswith("#")
+            ]
+    else:
+        lsNB = [
+            entry for entry in importlib_resources.contents('notebooks') if
+                not pkg_resources.resource_isdir('notebooks', entry)
+                and entry.endswith(".ipynb")
+                ]
+
+    print("Running notebooks:", " ".join(lsNB))
+
+    for entry in lsNB:
+        print("*****************************************************************")
+        print("Converting and running {}".format(entry))
+        print("*****************************************************************")
+
+        with path('notebooks', entry) as file_in:
+            out, err = run_python(" -m jupyter nbconvert --ExecutePreprocessor.timeout=120 " + 
+                "--execute --to notebook --inplace " + str(file_in))
+            sys.stderr.write(err)
+            sys.stderr.flush()
+            sys.stdout.write(out)
+            sys.stdout.flush()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file

notebooks/Scene_Editor.ipynb → notebooks/scene_editor.ipynb

 %% Cell type:markdown id: tags:
 
 # Railway Scene Editor
 
 %% Cell type:code id: tags:
 
 ``` python
 from IPython.core.display import display, HTML
 display(HTML("<style>.container { width:95% !important; }</style>"))
 ```
 
-%% Output
-
-
 %% Cell type:code id: tags:
 
 ``` python
 from flatland.utils.editor import EditorMVC
 mvc = EditorMVC(sGL="PILSVG" )
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Instructions
 - Drag to draw (improved dead-ends)
 - ctrl-click to add agent or select agent
   - if agent is selected:
     - ctrl-click to move agent position
     - use rotate agent to rotate 90°
     - ctrl-shift-click to set target for selected agent
       - target can be moved by repeating
 - to Resize the env (cannot preserve work):
   - select "Regen" tab, set regen size slider, click regenerate.
 - alt-click remove all rails from cell
 
 
 Demo Scene: complex_scene.pkl
 
 %% Cell type:code id: tags:
 
 ``` python
 mvc.view.display()
 ```
-
-%% Output
-
-
-%% Cell type:code id: tags:
-
-``` python
-```

notebooks/simple_example1_env_from_tuple.ipynb

-%% Cell type:markdown id: tags:
-
-### Example 1 - generate a rail from a manual specification
-From a map of tuples (cell_type, rotation)
-
-%% Cell type:code id: tags:
-
-``` python
-from flatland.envs.generators import rail_from_manual_specifications_generator
-from flatland.envs.observations import TreeObsForRailEnv
-from flatland.envs.rail_env import RailEnv
-from flatland.utils.rendertools import RenderTool
-from PIL import Image
-```
-
-%% Cell type:code id: tags:
-
-``` python
-specs = [[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)],
-         [(0, 0), (0, 0), (0, 0), (0, 0), (7, 0), (0, 0)],
-         [(7, 270), (1, 90), (1, 90), (1, 90), (2, 90), (7, 90)],
-         [(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)]]
-
-env = RailEnv(width=6,
-              height=4,
-              rail_generator=rail_from_manual_specifications_generator(specs),
-              number_of_agents=1,
-              obs_builder_object=TreeObsForRailEnv(max_depth=2))
-
-env.reset()
-
-env_renderer = RenderTool(env, gl="PILSVG")
-env_renderer.renderEnv(show=False)
-```
-
-%% Cell type:code id: tags:
-
-``` python
-Image.fromarray(env_renderer.gl.getImage())
-```
-
-%% Output
-
-
-    <PIL.Image.Image image mode=RGBA size=796x532 at 0x1BAB3E066D8>

notebooks/simple_example_3_manual_control.ipynb → notebooks/simple_example_manual_control.ipynb

 %% Cell type:markdown id: tags:
 
 ### Simple Example 3 - Manual Control
 
 By default this runs a few "move forward" actions for two agents, in a separate window.
 
 If you uncomment the "input" line below, it opens a text box in the Jupyter notebook, allowing basic manual control.
 
 eg Enter `"0 2 s<enter>"` to tell agent 0 to move forward, and step the environment.
 
 You should be able to see the red agent step forward, and get a reward from the env, looking like this:
 
 `Rewards:  {0: -1.0, 1: -1.0}   [done= {0: False, 1: False, '__all__': False} ]`
 
 Note that this example is set up to use the straightforward "PIL" renderer - without the special SBB artwork!
 The agent observations are displayed as squares of varying sizes, with a paler version of the agent colour.  The targets are half-size squares in the full agent colour.
 
 You can switch to the "PILSVG" renderer which is prettier but currently renders the agents one step behind, because it needs to know which way the agent is turning.  This can be confusing if you are debugging step-by-step.
 
 The image below is what the separate window should look like.
 
 %% Cell type:markdown id: tags:
 
 ![simple_example_3.png](simple_example_3.png)
 
 %% Cell type:code id: tags:
 
 ``` python
 import random
 import numpy as np
 import time
-from flatland.envs.generators import random_rail_generator
+```
+
+%% Cell type:code id: tags:
+
+``` python
+from IPython import display
+from ipycanvas import canvas
+```
+
+%% Cell type:code id: tags:
+
+``` python
+from flatland.envs.rail_generators import sparse_rail_generator
+from flatland.envs.line_generators import sparse_line_generator
 from flatland.envs.observations import TreeObsForRailEnv
+from flatland.envs.predictions import ShortestPathPredictorForRailEnv
 from flatland.envs.rail_env import RailEnv
 from flatland.utils.rendertools import RenderTool
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 random.seed(1)
 np.random.seed(1)
+```
+
+%% Cell type:code id: tags:
 
-env = RailEnv(width=7,
-              height=7,
-              rail_generator=random_rail_generator(),
-              number_of_agents=2,
-              obs_builder_object=TreeObsForRailEnv(max_depth=2))
+``` python
+nAgents = 3
+n_cities = 2
+max_rails_between_cities = 2
+max_rails_in_city = 4
+seed = 0
+env = RailEnv(
+        width=20,
+        height=30,
+        rail_generator=sparse_rail_generator(
+            max_num_cities=n_cities,
+            seed=seed,
+            grid_mode=True,
+            max_rails_between_cities=max_rails_between_cities,
+            max_rail_pairs_in_city=max_rails_in_city
+        ),
+        line_generator=sparse_line_generator(),
+        number_of_agents=nAgents,
+        obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv())
+    )
+
+init_observation = env.reset()
+```
 
+%% Cell type:code id: tags:
+
+``` python
 # Print the observation vector for agent 0
 obs, all_rewards, done, _ = env.step({0: 0})
 for i in range(env.get_num_agents()):
     env.obs_builder.util_print_obs_subtree(tree=obs[i])
 
 env_renderer = RenderTool(env, gl="PIL")
-# env_renderer = RenderTool(env, gl="PILSVG")
-
-env_renderer.renderEnv(show=True, frames=True)
+env_renderer.render_env(show=True, frames=True)
 
 print("Manual control: s=perform step, q=quit, [agent id] [1-2-3 action] \
        (turnleft+move, move to front, turnright+move)")
 ```
 
-%% Output
-
-     [0, 0, 0, 0, 3.0, 0, 0]
-      L: [0, -inf, -inf, -inf, -inf, -inf, -inf]
-        L: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        F: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        R: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        B: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-      F: [-inf, inf, inf, inf, 2, 1.0, 0]
-        L: [0, 0, 1, inf, inf, 3, 0]
-        F: [0, 0, inf, inf, inf, 5, 8.0]
-        R: [0, 0, -inf, -inf, -inf, -inf, -inf]
-        B: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-      R: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        L: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        F: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        R: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        B: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-      B: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        L: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        F: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        R: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        B: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-     [0, 0, 0, 0, 8.0, 0, 0]
-      L: [0, -inf, -inf, -inf, -inf, -inf, -inf]
-        L: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        F: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        R: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        B: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-      F: [-inf, inf, inf, inf, 2, 6.0, 0]
-        L: [0, 0, -inf, -inf, -inf, -inf, -inf]
-        F: [-inf, -inf, inf, inf, inf, 3, 5.0]
-        R: [0, 0, inf, inf, inf, 6, 6.0]
-        B: [0, 0, -inf, -inf, -inf, -inf, -inf]
-      R: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        L: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        F: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        R: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        B: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-      B: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        L: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        F: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        R: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-        B: [-inf, -inf, -inf, -inf, -inf, -inf, -inf]
-    Manual control: s=perform step, q=quit, [agent id] [1-2-3 action]        (turnleft+move, move to front, turnright+move)
-
 %% Cell type:code id: tags:
 
 ``` python
+image_arr = env_renderer.get_image()
+oCanvas = canvas.Canvas()
+oCanvas.put_image_data(image_arr[:,:,0:3])
+display.display(oCanvas)
+
 for step in range(10):
 
     # This is an example command, setting agent 0's action to 2 (move forward), and agent 1's action to 2,
     # then stepping the environment.
     cmd = "0 2 1 2 s"
 
     # uncomment this input statement if you want to try interactive manual commands
     # cmd = input(">> ")
 
     cmds = cmd.split(" ")
 
     action_dict = {}
 
     i = 0
     while i < len(cmds):
         if cmds[i] == 'q':
             import sys
 
             sys.exit()
         elif cmds[i] == 's':
             obs, all_rewards, done, _ = env.step(action_dict)
             action_dict = {}
             print("Rewards: ", all_rewards, "  [done=", done, "]")
         else:
             agent_id = int(cmds[i])
             action = int(cmds[i + 1])
             action_dict[agent_id] = action
             i = i + 1
         i += 1
 
-    env_renderer.renderEnv(show=True, frames=True)
-
-    time.sleep(0.3)
+    env_renderer.render_env(show=True, frames=True)
+    gIm = env_renderer.get_image()
+    oCanvas.put_image_data(gIm[:,:,0:3])
+    time.sleep(0.1)
 ```
-
-%% Output
-
-    Rewards:  {0: -1.0, 1: -1.0}   [done= {0: False, 1: False, '__all__': False} ]
-    Rewards:  {0: -1.0, 1: -1.0}   [done= {0: False, 1: False, '__all__': False} ]
-    Rewards:  {0: -1.0, 1: -1.0}   [done= {0: False, 1: False, '__all__': False} ]
-    Rewards:  {0: -1.0, 1: 0}   [done= {0: False, 1: False, '__all__': False} ]
-    Rewards:  {0: -1.0, 1: 0}   [done= {0: False, 1: False, '__all__': False} ]
-    Rewards:  {0: -1.0, 1: 0}   [done= {0: False, 1: False, '__all__': False} ]
-    Rewards:  {0: -1.0, 1: 0}   [done= {0: False, 1: False, '__all__': False} ]
-    Rewards:  {0: 0, 1: 0}   [done= {0: False, 1: False, '__all__': False} ]
-    Rewards:  {0: 0, 1: 0}   [done= {0: False, 1: False, '__all__': False} ]
-    Rewards:  {0: 0, 1: 0}   [done= {0: False, 1: False, '__all__': False} ]

notebooks/Simple_Rendering_Demo.ipynb → notebooks/simple_rendering_demo.ipynb

 %% Cell type:markdown id: tags:
 
 # Plot the Env and some trees
 
 %% Cell type:code id: tags:
 
 ``` python
 %load_ext autoreload
 %autoreload 2
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 import jpy_canvas
 import random
 import time
 import sys
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # in case you need to tweak your PYTHONPATH...
 sys.path.append("../flatland")
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 import flatland.core.env
 import flatland.utils.rendertools as rt
-from flatland.envs.rail_env import RailEnv, random_rail_generator
+from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_generators import sparse_rail_generator
+from flatland.envs.line_generators import sparse_line_generator
 from flatland.envs.observations import TreeObsForRailEnv
+from flatland.envs.predictions import ShortestPathPredictorForRailEnv
 ```
 
-%% Cell type:code id: tags:
-
-``` python
-from IPython.core.display import display, HTML
-display(HTML("<style>.container { width:90% !important; }</style>"))
-```
-
-%% Output
-
-
 %% Cell type:markdown id: tags:
 
 # Generate
 
 %% Cell type:code id: tags:
 
 ``` python
 nAgents = 3
-fnMethod = random_rail_generator(cell_type_relative_proportion=[1] * 11)
-env = RailEnv(width=20,
-                  height=10,
-                  rail_generator=fnMethod,
-                  number_of_agents=nAgents,
-                  obs_builder_object=TreeObsForRailEnv(max_depth=2))
+n_cities = 2
+max_rails_between_cities = 2
+max_rails_in_city = 4
+seed = 0
+env = RailEnv(
+        width=20,
+        height=30,
+        rail_generator=sparse_rail_generator(
+            max_num_cities=n_cities,
+            seed=seed,
+            grid_mode=True,
+            max_rails_between_cities=max_rails_between_cities,
+            max_rail_pairs_in_city=max_rails_in_city
+        ),
+        line_generator=sparse_line_generator(),
+        number_of_agents=nAgents,
+        obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv())
+    )
+
+init_observation = env.reset()
 ```
 
 %% Cell type:markdown id: tags:
 
 # Render
 
 %% Cell type:code id: tags:
 
 ``` python
 oRT = rt.RenderTool(env,gl="PILSVG")
-oRT.renderEnv()
-img = oRT.getImage()
+oRT.render_env(show_observations=False,show_predictions=True)
+img = oRT.get_image()
 ```
 
-%% Output
+%% Cell type:code id: tags:
 
-    Illegal target rail: 3 13 0001011000000001
-    Illegal target rail: 5 13 0100000000000010
+``` python
+jpy_canvas.Canvas(img)
+```
 
 %% Cell type:code id: tags:
 
 ``` python
-jpy_canvas.Canvas(img)
+oRT = rt.RenderTool(env,gl="PIL")
+oRT.render_env(show_observations=False,show_predictions=True)
+img = oRT.get_image()
 ```
 
-%% Output
+%% Cell type:code id: tags:
 
+``` python
+jpy_canvas.Canvas(img)
+```

notebooks/test-service-timeouts.ipynb

+%% Cell type:markdown id: tags:
+
+# Test Service
+
+Intended to test the service.py evaluator.
+Runs the service.py and a simple client.
+
+%% Cell type:markdown id: tags:
+
+Setup
+---
+
+%% Cell type:code id: tags:
+
+``` python
+%load_ext autoreload
+%autoreload 2
+```
+
+%% Cell type:code id: tags:
+
+``` python
+from IPython.core.display import display, HTML
+display(HTML("<style>.container { width:90% !important; }</style>"))
+```
+
+%% Cell type:code id: tags:
+
+``` python
+import os
+import redis
+import subprocess as sp
+import shlex
+import time
+```
+
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+```
+
+%% Cell type:code id: tags:
+
+``` python
+from flatland.core.env_observation_builder import DummyObservationBuilder
+from flatland.envs.persistence import RailEnvPersister
+from flatland.evaluators.client import FlatlandRemoteClient
+from flatland.evaluators.client import TimeoutException
+```
+
+%% Cell type:code id: tags:
+
+``` python
+sDirRoot = "/Users/flaurent/Sites/flatland/neurips2020-flatland-starter-kit/scratch/test-neurips2020-round2-v0"
+```
+
+%% Cell type:code id: tags:
+
+``` python
+!ps -ef | grep -i python | grep -i flatland.evaluators.service
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def kill_evaluator():
+    # kill previous evaluator
+    !ps -ef | grep -i python | grep -i flatland.evaluators.service | awk '{print $2}' | xargs kill
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def cleanup():
+    oRedis = redis.Redis()
+    lKeys = oRedis.keys("flatland*")
+    for sKey in lKeys:
+        print("Deleting:", sKey)
+        oRedis.delete(sKey)
+
+    !rm -f /tmp/output.csv
+
+    kill_evaluator()
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def configure_env(overall=8*60*60, planning=5*60, step=10, successive=10, mean_percentage=0.25):
+    osEnv2 = os.environ.copy()
+    osEnv2["FLATLAND_OVERALL_TIMEOUT"]=str(overall)
+    osEnv2["FLATLAND_PER_STEP_TIMEOUT"] = str(step)
+    osEnv2["FLATLAND_INITIAL_PLANNING_TIMEOUT"] = str(planning)
+    osEnv2["FLATLAND_MAX_SUCCESSIVE_TIMEOUTS"] = str(successive)
+    osEnv2["TEST_MIN_PERCENTAGE_COMPLETE_MEAN"] = str(mean_percentage)
+    return osEnv2
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def start_evaluator(env):
+    sCmd = f"python -m flatland.evaluators.service --test_folder {sDirRoot} --pickle" # --verbose"
+    lsCmd = shlex.split(sCmd)
+    print(sCmd)
+
+    oPipe = sp.Popen(lsCmd, env=env)
+    oPipe.poll()
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def start_client():
+    oFRC = FlatlandRemoteClient(test_envs_root=sDirRoot, verbose=False, use_pickle=True)
+    env, env_dict = RailEnvPersister.load_new(f"{sDirRoot}/Test_0/Level_0.pkl")
+    return oFRC
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def random_controller(obs, _env):
+    np.random.seed(0)
+
+    dAct = {}
+    for iAg in range(len(_env.agents)):
+        dAct[iAg] = np.random.randint(0, 5)
+    return dAct
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def run_submission(oFRC, slow_ep=-1, debug=False):
+    def log(txt, end="\n"):
+        if debug: print(txt, end)
+
+    dummy_obs = DummyObservationBuilder()
+    episode = 0
+    obs = True
+
+    while obs:
+        obs, info = oFRC.env_create(obs_builder_object=dummy_obs)
+        log(oFRC.current_env_path)
+        log(f"Episode : {episode}")
+
+        if not obs:
+            log("None observation - all envs completed!")
+            break
+
+        while True:
+            action = random_controller(obs, oFRC.env)
+
+            if slow_ep != -1:
+                # make a specific episode artificially slow
+                if (episode == slow_ep) and (oFRC.env._elapsed_steps > 10):
+                    time.sleep(2)
+
+            try:
+                observation, all_rewards, done, info = oFRC.env_step(action)
+                #log(".", end="")
+                if done['__all__']:
+                    log("\nCompleted Episode : ", episode)
+                    log("Reward : ", sum(list(all_rewards.values())))
+                    break
+            except TimeoutException as err:
+                log("Timeout: ", err)
+                break
+
+        episode += 1
+```
+
+%% Cell type:markdown id: tags:
+
+Tests
+---
+
+%% Cell type:code id: tags:
+
+``` python
+%%time
+
+# Normal submission
+cleanup()
+config = configure_env(overall=8*60*60, planning=5*60, step=10, successive=10, mean_percentage=0.1)
+start_evaluator(config)
+client = start_client()
+run_submission(client)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+%%time
+
+# Overall timeout
+cleanup()
+config = configure_env(overall=3, planning=5*60, step=10, successive=10, mean_percentage=0.0)
+start_evaluator(config)
+client = start_client()
+run_submission(client, debug=False)
+```
+
+%% Cell type:markdown id: tags:
+
+Cleanup
+---
+
+%% Cell type:code id: tags:
+
+``` python
+kill_evaluator()
+```