diff --git a/examples/flatland_2_0_example.py b/examples/flatland_2_0_example.py
index a1ad9a85d9a41f57c317a0b4b0bc61796e4e0f4a..da8cacef2a3e9972ef4b90094c59f448ca07bc37 100644
--- a/examples/flatland_2_0_example.py
+++ b/examples/flatland_2_0_example.py
@@ -13,7 +13,7 @@ np.random.seed(1)
# Training on simple small tasks is the best way to get familiar with the environment
# Use a the malfunction generator to break agents from time to time
-stochastic_data = {'prop_malfunction': 0.1, # Percentage of defective agents
+stochastic_data = {'prop_malfunction': 1., # Percentage of defective agents
'malfunction_rate': 30, # Rate of malfunction occurence
'min_duration': 3, # Minimal duration of malfunction
'max_duration': 20 # Max duration of malfunction
@@ -31,11 +31,11 @@ speed_ration_map = {1.: 0.25, # Fast passenger train
env = RailEnv(width=50,
height=50,
rail_generator=sparse_rail_generator(num_cities=25, # Number of cities in map (where train stations are)
- num_intersections=0, # Number of intersections (no start / target)
+ num_intersections=10, # Number of intersections (no start / target)
num_trainstations=50, # Number of possible start/targets on map
min_node_dist=3, # Minimal distance of nodes
- node_radius=2, # Proximity of stations to city center
- num_neighb=3, # Number of connections to other cities/intersections
+ node_radius=4, # Proximity of stations to city center
+ num_neighb=4, # Number of connections to other cities/intersections
seed=15, # Random seed
grid_mode=True,
enhance_intersection=False
diff --git a/flatland/core/transition_map.py b/flatland/core/transition_map.py
index bb954998688772a7ce69e5228cff3e16d037f2af..232d6fdab02c57da95bf04c631e4905986c71327 100644
--- a/flatland/core/transition_map.py
+++ b/flatland/core/transition_map.py
@@ -8,6 +8,7 @@ from numpy import array
from flatland.core.grid.grid4 import Grid4Transitions
from flatland.core.grid.grid4_utils import get_new_position
+from flatland.core.grid.rail_env_grid import RailEnvTransitions
from flatland.core.transitions import Transitions
@@ -482,6 +483,14 @@ class GridTransitionMap(TransitionMap):
grcPos = array(rcPos)
grcMax = self.grid.shape
+ # Transition elements
+ transitions = RailEnvTransitions()
+ cells = transitions.transition_list
+ simple_switch_east_south = transitions.rotate_transition(cells[10], 90)
+ simple_switch_west_south = transitions.rotate_transition(cells[2], 270)
+ symmetrical = cells[6]
+ double_slip = cells[5]
+ three_way_transitions = [simple_switch_east_south, simple_switch_west_south, symmetrical]
# loop over available outbound directions (indices) for rcPos
self.set_transitions(rcPos, 0)
@@ -517,25 +526,18 @@ class GridTransitionMap(TransitionMap):
self.set_transition((rcPos[0], rcPos[1], mirror(connect_directions[0])), connect_directions[1], 1)
self.set_transition((rcPos[0], rcPos[1], mirror(connect_directions[1])), connect_directions[0], 1)
- # Find feasible connection fro three entries
+ # Find feasible connection for three entries
if number_of_incoming == 3:
+ transition = np.random.choice(three_way_transitions, 1)
hole = np.argwhere(incoming_connections < 1)[0][0]
- connect_directions = [(hole + 1) % 4, (hole + 2) % 4, (hole + 3) % 4]
- self.set_transition((rcPos[0], rcPos[1], mirror(connect_directions[0])), connect_directions[1], 1)
- self.set_transition((rcPos[0], rcPos[1], mirror(connect_directions[0])), connect_directions[2], 1)
- self.set_transition((rcPos[0], rcPos[1], mirror(connect_directions[1])), connect_directions[0], 1)
- self.set_transition((rcPos[0], rcPos[1], mirror(connect_directions[2])), connect_directions[0], 1)
- # Make a cross
+ transition = transitions.rotate_transition(transition, int(hole * 90))
+ self.set_transitions((rcPos[0], rcPos[1]), transition)
+
+ # Make a double slip switch
if number_of_incoming == 4:
- connect_directions = np.arange(4)
- self.set_transition((rcPos[0], rcPos[1], connect_directions[0]), connect_directions[0], 1)
- self.set_transition((rcPos[0], rcPos[1], connect_directions[0]), connect_directions[1], 1)
- self.set_transition((rcPos[0], rcPos[1], connect_directions[1]), connect_directions[0], 1)
- self.set_transition((rcPos[0], rcPos[1], connect_directions[1]), connect_directions[1], 1)
- self.set_transition((rcPos[0], rcPos[1], connect_directions[2]), connect_directions[2], 1)
- self.set_transition((rcPos[0], rcPos[1], connect_directions[2]), connect_directions[3], 1)
- self.set_transition((rcPos[0], rcPos[1], connect_directions[3]), connect_directions[2], 1)
- self.set_transition((rcPos[0], rcPos[1], connect_directions[3]), connect_directions[3], 1)
+ rotation = np.random.randint(2)
+ transition = transitions.rotate_transition(double_slip, int(rotation * 90))
+ self.set_transitions((rcPos[0], rcPos[1]), transition)
return True
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index 280fd345d8c1db206c42dc30ba2d7b5fa2e8a69e..d59ca7dc8f15d7f550e59f426a34cdd3275d954a 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -266,7 +266,8 @@ class RailEnv(Environment):
def _agent_malfunction(self, agent):
# Decrease counter for next event
- agent.malfunction_data['next_malfunction'] -= 1
+ if agent.malfunction_data['malfunction_rate'] > 0:
+ agent.malfunction_data['next_malfunction'] -= 1
# Only agents that have a positive rate for malfunctions and are not currently broken are considered
if agent.malfunction_data['malfunction_rate'] > 0 >= agent.malfunction_data['malfunction']:
@@ -294,7 +295,8 @@ class RailEnv(Environment):
alpha = 1.0
beta = 1.0
-
+ # Epsilon to avoid rounding errors
+ epsilon = 0.01
invalid_action_penalty = 0 # previously -2; GIACOMO: we decided that invalid actions will carry no penalty
step_penalty = -1 * alpha
global_reward = 1 * beta
@@ -310,7 +312,6 @@ class RailEnv(Environment):
self.rewards_dict = {i: r + global_reward for i, r in self.rewards_dict.items()}
return self._get_observations(), self.rewards_dict, self.dones, {}
- # for i in range(len(self.agents_handles)):
for i_agent in range(self.get_num_agents()):
agent = self.agents[i_agent]
agent.old_direction = agent.direction
@@ -328,15 +329,22 @@ class RailEnv(Environment):
# The train is broken
if agent.malfunction_data['malfunction'] > 0:
- agent.malfunction_data['malfunction'] -= 1
+ # Last step of malfunction --> Agent starts moving again after getting fixed
+ if agent.malfunction_data['malfunction'] < 2:
+ agent.malfunction_data['malfunction'] -= 1
+ self.agents[i_agent].moving = True
+ action_dict[i_agent] = RailEnvActions.DO_NOTHING
- # Broken agents are stopped
- self.rewards_dict[i_agent] += step_penalty * agent.speed_data['speed']
- self.agents[i_agent].moving = False
- action_dict[i_agent] = RailEnvActions.DO_NOTHING
+ else:
+ agent.malfunction_data['malfunction'] -= 1
- # Nothing left to do with broken agent
- continue
+ # Broken agents are stopped
+ self.rewards_dict[i_agent] += step_penalty * agent.speed_data['speed']
+ self.agents[i_agent].moving = False
+ action_dict[i_agent] = RailEnvActions.DO_NOTHING
+
+ # Nothing left to do with broken agent
+ continue
if action_dict[i_agent] < 0 or action_dict[i_agent] > len(RailEnvActions):
print('ERROR: illegal action=', action_dict[i_agent],
@@ -350,7 +358,8 @@ class RailEnv(Environment):
# Keep moving
action = RailEnvActions.MOVE_FORWARD
- if action == RailEnvActions.STOP_MOVING and agent.moving and agent.speed_data['position_fraction'] == 0.:
+ if action == RailEnvActions.STOP_MOVING and agent.moving and agent.speed_data[
+ 'position_fraction'] <= epsilon:
# Only allow halting an agent on entering new cells.
agent.moving = False
self.rewards_dict[i_agent] += stop_penalty
@@ -372,7 +381,7 @@ class RailEnv(Environment):
# If the agent can make an action
action_selected = False
- if agent.speed_data['position_fraction'] == 0.:
+ if agent.speed_data['position_fraction'] <= epsilon:
if action != RailEnvActions.DO_NOTHING and action != RailEnvActions.STOP_MOVING:
cell_free, new_cell_valid, new_direction, new_position, transition_valid = \
self._check_action_on_agent(action, agent)
diff --git a/flatland/envs/rail_generators.py b/flatland/envs/rail_generators.py
index 39796515f73c7702ba4dc162301a63b0186dc1d3..c23593463c2b679cfd09fcbcf390c3d5a05acde4 100644
--- a/flatland/envs/rail_generators.py
+++ b/flatland/envs/rail_generators.py
@@ -563,7 +563,6 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
node_positions = []
city_positions = []
intersection_positions = []
-
# Evenly distribute cities and intersections
if grid_mode:
tot_num_node = num_intersections + num_cities
@@ -572,10 +571,7 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
nodes_per_col = int(np.ceil(tot_num_node / nodes_per_row))
x_positions = np.linspace(node_radius, height - node_radius, nodes_per_row, dtype=int)
y_positions = np.linspace(node_radius, width - node_radius, nodes_per_col, dtype=int)
-
- fraction = 0
- city_fraction = num_cities / tot_num_node
- step = np.gcd(num_intersections, num_cities) / tot_num_node
+ city_idx = np.random.choice(np.arange(tot_num_node), num_cities)
for node_idx in range(num_cities + num_intersections):
to_close = True
@@ -608,10 +604,9 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
warnings.warn("Could not set nodes, please change initial parameters!!!!")
break
else:
- fraction = (fraction + step) % 1.
x_tmp = x_positions[node_idx % nodes_per_row]
y_tmp = y_positions[node_idx // nodes_per_row]
- if len(city_positions) < num_cities and fraction < city_fraction:
+ if node_idx in city_idx:
city_positions.append((x_tmp, y_tmp))
else:
intersection_positions.append((x_tmp, y_tmp))
diff --git a/flatland/envs/schedule_generators.py b/flatland/envs/schedule_generators.py
index 7a116f9e6b5a233112f2e4b5b73556f13b761be6..e81da924d1157fa9d5deef56306643c6a356cc8d 100644
--- a/flatland/envs/schedule_generators.py
+++ b/flatland/envs/schedule_generators.py
@@ -229,7 +229,6 @@ def schedule_from_file(filename) -> ScheduleGenerator:
# agents are always reset as not moving
if len(data['agents_static'][0]) > 5:
- print(len(data['agents_static'][0]))
agents_static = [EnvAgentStatic(d[0], d[1], d[2], d[3], d[4], d[5]) for d in data["agents_static"]]
else:
agents_static = [EnvAgentStatic(d[0], d[1], d[2], d[3]) for d in data["agents_static"]]