From 6f8e4f379e6e8b296bc65d6a9b66df295834dfbb Mon Sep 17 00:00:00 2001
From: MLErik <baerenjesus@gmail.com>
Date: Fri, 16 Aug 2019 17:58:13 -0400
Subject: [PATCH] introduced cities and intersections. These will make up the
sparse network. THen you can add trainstations to the cities and populate
them with tasks for agents (start/target). Orientation of agents needs to be
fixed. Also check for invalid transitions in cities and nodes needs to be
implemented.
---
flatland/envs/generators.py | 135 ++++++++++++------
...test_flatland_env_sparse_rail_generator.py | 25 ++--
2 files changed, 105 insertions(+), 55 deletions(-)
diff --git a/flatland/envs/generators.py b/flatland/envs/generators.py
index d63e161a..be21f86c 100644
--- a/flatland/envs/generators.py
+++ b/flatland/envs/generators.py
@@ -9,7 +9,7 @@ from flatland.core.grid.grid_utils import distance_on_rail
from flatland.core.grid.rail_env_grid import RailEnvTransitions
from flatland.core.transition_map import GridTransitionMap
from flatland.envs.agent_utils import EnvAgentStatic
-from flatland.envs.grid4_generators_utils import connect_rail, connect_from_nodes, connect_nodes, connect_to_nodes
+from flatland.envs.grid4_generators_utils import connect_rail, connect_from_nodes, connect_nodes
from flatland.envs.grid4_generators_utils import get_rnd_agents_pos_tgt_dir_on_rail
@@ -776,12 +776,13 @@ def realistic_rail_generator(nr_start_goal=1, seed=0):
return generator
-def sparse_rail_generator(nr_nodes=100, max_neigbours=2, min_node_dist=20, node_radius=2,
+def sparse_rail_generator(num_cities=100, num_intersections=10, num_trainstations=2, min_node_dist=20, node_radius=2,
+ num_neighb=4,
seed=0):
'''
:param nr_train_stations:
- :param nr_nodes:
+ :param num_cities:
:param mean_node_neighbours:
:param min_node_dist:
:param seed:
@@ -789,6 +790,11 @@ def sparse_rail_generator(nr_nodes=100, max_neigbours=2, min_node_dist=20, node_
'''
def generator(width, height, num_agents, num_resets=0):
+
+ if num_agents > num_trainstations:
+ num_agents = num_trainstations
+ warnings.warn("complex_rail_generator: num_agents > nr_start_goal, changing num_agents")
+
rail_trans = RailEnvTransitions()
grid_map = GridTransitionMap(width=width, height=height, transitions=rail_trans)
rail_array = grid_map.grid
@@ -797,12 +803,12 @@ def sparse_rail_generator(nr_nodes=100, max_neigbours=2, min_node_dist=20, node_
# Generate a set of nodes for the sparse network
node_positions = []
- for node_idx in range(nr_nodes):
+ for node_idx in range(num_cities + num_intersections):
to_close = True
tries = 0
while to_close:
- x_tmp = np.random.randint(height)
- y_tmp = np.random.randint(width)
+ x_tmp = 1 + np.random.randint(height - 1)
+ y_tmp = 1 + np.random.randint(width - 1)
to_close = False
for node_pos in node_positions:
if distance_on_rail((x_tmp, y_tmp), node_pos) < min_node_dist:
@@ -815,7 +821,7 @@ def sparse_rail_generator(nr_nodes=100, max_neigbours=2, min_node_dist=20, node_
break
# Chose node connection
- available_nodes = np.arange(nr_nodes)
+ available_nodes = np.arange(num_cities + num_intersections)
current_node = 0
node_stack = [current_node]
@@ -824,61 +830,98 @@ def sparse_rail_generator(nr_nodes=100, max_neigbours=2, min_node_dist=20, node_
delete_idx = np.where(available_nodes == current_node)
available_nodes = np.delete(available_nodes, delete_idx, 0)
- # Get random number of neighbors
- num_neighb = 2 # np.random.randint(1, max_neigbours)
+ # Sort available neighbors according to their distance.
+ node_dist = []
+ for av_node in available_nodes:
+ node_dist.append(distance_on_rail(node_positions[current_node], node_positions[av_node]))
+ available_nodes = available_nodes[np.argsort(node_dist)]
+
+ # Set number of neighboring nodes
+ # np.random.randint(1, max_neigbours)
+
if len(available_nodes) >= num_neighb:
- connected_neighb_idx = np.random.choice(available_nodes, num_neighb, replace=False)
+ connected_neighb_idx = available_nodes[
+ 0:2] # np.random.choice(available_nodes, num_neighb, replace=False)
else:
connected_neighb_idx = available_nodes
+ # Connect to the neighboring nodes
for neighb in connected_neighb_idx:
if neighb not in node_stack:
node_stack.append(neighb)
new_path = connect_nodes(rail_trans, rail_array, node_positions[current_node], node_positions[neighb])
node_stack.pop(0)
- # Generate start and target node directory for all agents
+ # Place train stations close to the node
+ train_stations = [[] for i in range(num_cities)]
+
+ for station in range(num_trainstations):
+ trainstation_node = int(station / num_trainstations * num_cities)
+
+ station_x = np.clip(node_positions[trainstation_node][0] + np.random.randint(-node_radius, node_radius), 0,
+ height - 1)
+ station_y = np.clip(node_positions[trainstation_node][1] + np.random.randint(-node_radius, node_radius), 0,
+ width - 1)
+ while (station_x, station_y) in train_stations or (station_x, station_y) == node_positions[
+ trainstation_node] or \
+ rail_array[(station_x, station_y)] != 0:
+ station_x = np.clip(node_positions[trainstation_node][0] + np.random.randint(-node_radius, node_radius),
+ 0,
+ height - 1)
+ station_y = np.clip(node_positions[trainstation_node][1] + np.random.randint(-node_radius, node_radius),
+ 0,
+ width - 1)
+ train_stations[trainstation_node].append((station_x, station_y))
+
+ # Connect train station to the correct node
+ new_path = connect_from_nodes(rail_trans, rail_array, node_positions[trainstation_node],
+ (station_x, station_y))
+
+ # Generate start and target node directory for all agents.
+ # Assure that start and target are not in the same node
agent_start_targets_nodes = []
+
+ # Slot availability in node
+ node_available_start = []
+ node_available_target = []
+ for node_idx in range(num_cities):
+ node_available_start.append(len(train_stations[node_idx]))
+ node_available_target.append(len(train_stations[node_idx]))
+
+ # Assign agents to slots
for agent_idx in range(num_agents):
- start_target_tuple = np.random.choice(nr_nodes, 2, replace=False)
- agent_start_targets_nodes.append(start_target_tuple)
+ av_start_nodes = [idx for idx, val in enumerate(node_available_start) if val > 0]
+ av_target_nodes = [idx for idx, val in enumerate(node_available_target) if val > 0]
+ start_node = np.random.choice(av_start_nodes)
+ target_node = np.random.choice(av_target_nodes)
+ while target_node == start_node:
+ target_node = np.random.choice(av_target_nodes)
+ node_available_start[start_node] -= 1
+ node_available_target[target_node] -= 1
+ print(node_available_target, node_available_start)
+
+ agent_start_targets_nodes.append((start_node, target_node))
+
+ # Place agents and targets within available train stations
- # Generate actual start and target locations from around nodes
agents_position = []
agents_target = []
agents_direction = []
- agent_idx = 0
- for start_target in agent_start_targets_nodes:
- start_x = np.clip(node_positions[start_target[0]][0] + np.random.randint(-node_radius, node_radius), 0,
- height - 1)
- start_y = np.clip(node_positions[start_target[0]][1] + np.random.randint(-node_radius, node_radius), 0,
- width - 1)
- target_x = np.clip(node_positions[start_target[1]][0] + np.random.randint(-node_radius, node_radius), 0,
- height - 1)
- target_y = np.clip(node_positions[start_target[1]][1] + np.random.randint(-node_radius, node_radius), 0,
- width - 1)
- # Make sure we don't put to starts or targets on same cell
- while (start_x, start_y) in agents_position or (start_x, start_y) == node_positions[start_target[0]]:
- start_x = np.clip(node_positions[start_target[0]][0] + np.random.randint(-node_radius, node_radius),
- 0,
- height - 1)
- start_y = np.clip(node_positions[start_target[0]][1] + np.random.randint(-node_radius, node_radius),
- 0,
- width - 1)
- while (target_x, target_y) in agents_target or (target_x, target_y) == node_positions[start_target[1]] or \
- rail_array[(target_x, target_y)] != 0:
- target_x = np.clip(
- node_positions[start_target[1]][0] + np.random.randint(-node_radius, node_radius), 0,
- height - 1)
- target_y = np.clip(
- node_positions[start_target[1]][1] + np.random.randint(-node_radius, node_radius), 0,
- width - 1)
- agents_position.append((start_x, start_y))
- agents_target.append((target_x, target_y))
- new_path = connect_to_nodes(rail_trans, rail_array, agents_position[agent_idx],
- node_positions[start_target[0]])
- new_path = connect_from_nodes(rail_trans, rail_array, node_positions[start_target[1]],
- agents_target[agent_idx])
+
+ for agent_idx in range(num_agents):
+ current_start_node = agent_start_targets_nodes[agent_idx][0]
+ current_target_node = agent_start_targets_nodes[agent_idx][1]
+ target_station_idx = np.random.randint(len(train_stations[current_target_node]))
+ start_station_idx = np.random.randint(len(train_stations[current_start_node]))
+ target = train_stations[current_target_node][target_station_idx]
+ start = train_stations[current_start_node][start_station_idx]
+ while (target[0], target[1]) in agents_target:
+ target_station_idx = np.random.randint(len(train_stations[current_target_node]))
+ start_station_idx = np.random.randint(len(train_stations[current_start_node]))
+ target = train_stations[current_target_node][target_station_idx]
+ start = train_stations[current_start_node][start_station_idx]
+ agents_position.append((start[0], start[1]))
+ agents_target.append((target[0], target[1]))
agents_direction.append(0)
agent_idx += 1
diff --git a/tests/test_flatland_env_sparse_rail_generator.py b/tests/test_flatland_env_sparse_rail_generator.py
index 49739297..2a361ca9 100644
--- a/tests/test_flatland_env_sparse_rail_generator.py
+++ b/tests/test_flatland_env_sparse_rail_generator.py
@@ -1,11 +1,12 @@
import time
-from flatland.envs.generators import sparse_rail_generator,realistic_rail_generator
+import numpy as np
+
+from flatland.envs.generators import sparse_rail_generator, realistic_rail_generator
from flatland.envs.observations import GlobalObsForRailEnv
from flatland.envs.rail_env import RailEnv
from flatland.utils.rendertools import RenderTool
-import numpy as np
def test_realistic_rail_generator():
for test_loop in range(5):
@@ -22,14 +23,20 @@ def test_realistic_rail_generator():
env_renderer.close_window()
def test_sparse_rail_generator():
-
- env = RailEnv(width=20,
- height=20,
- rail_generator=sparse_rail_generator(nr_nodes=3, min_node_dist=8,
- node_radius=4),
- number_of_agents=15,
+ env = RailEnv(width=50,
+ height=50,
+ rail_generator=sparse_rail_generator(num_cities=10, # Number of cities in map
+ num_intersections=3, # Number of interesections in map
+ num_trainstations=30, # Number of possible start/targets on map
+ min_node_dist=10, # Minimal distance of nodes
+ node_radius=2, # Proximity of stations to city center
+ num_neighb=4, # Number of connections to other cities
+ seed=15, # Random seed
+ ),
+ number_of_agents=20,
obs_builder_object=GlobalObsForRailEnv())
# reset to initialize agents_static
env_renderer = RenderTool(env, gl="PILSVG", )
env_renderer.render_env(show=True, show_observations=True, show_predictions=False)
- time.sleep(2)
+ env_renderer.gl.save_image("flatalnd_2_0.png")
+ time.sleep(100)
--
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