def reset(self):
obs, info = self.rail_env.reset(regenerate_rail=self._regenerate_rail_on_reset,
regenerate_schedule=self._regenerate_schedule_on_reset)
# Reset rendering
if self.render:
self.env_renderer = RenderTool(self.rail_env, gl="PGL")
self.env_renderer.set_new_rail()
# Reset custom observations
self.observation_normalizer.reset_custom_obs(self.rail_env)
# Compute deadlocks
self.deadlocks_detector.reset(self.rail_env.get_num_agents())
info["deadlocks"] = {}
for agent in range(self.rail_env.get_num_agents()):
info["deadlocks"][agent] = self.deadlocks_detector.deadlocks[agent]
# Normalization
for agent in obs:
if obs[agent] is not None:
obs[agent] = self.observation_normalizer.normalize_observation(obs[agent], self.rail_env,
agent, info["deadlocks"][agent])
return obs, info
def demo(args=None):
"""Demo script to check installation"""
env = RailEnv(width=15,
height=15,
rail_generator=complex_rail_generator(nr_start_goal=10,
nr_extra=1,
min_dist=8,
max_dist=99999),
schedule_generator=complex_schedule_generator(),
number_of_agents=5)
env._max_episode_steps = int(15 * (env.width + env.height))
env_renderer = RenderTool(env)
while True:
obs, info = env.reset()
_done = False
# Run a single episode here
step = 0
while not _done:
# Compute Action
_action = {}
for _idx, _ in enumerate(env.agents):
_action[_idx] = np.random.randint(0, 5)
obs, all_rewards, done, _ = env.step(_action)
_done = done['__all__']
step += 1
env_renderer.render_env(show=True,
frames=False,
show_observations=False,
show_predictions=False)
time.sleep(0.3)
return 0
def render_env(env, fname):
env_renderer = RenderTool(env, gl="PGL")
env_renderer.render_env()
image = env_renderer.get_image()
pil_image = PIL.Image.fromarray(image)
pil_image.save(fname)
def test_path_not_exists(rendering=False):
rail, rail_map = make_simple_rail_unconnected()
env = RailEnv(
width=rail_map.shape[1],
height=rail_map.shape[0],
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(),
number_of_agents=1,
obs_builder_object=TreeObsForRailEnv(
max_depth=2, predictor=ShortestPathPredictorForRailEnv()),
)
env.reset()
check_path(
env,
rail,
(5, 6), # south dead-end
0, # north
(0, 3), # north dead-end
False)
if rendering:
renderer = RenderTool(env, gl="PILSVG")
renderer.render_env(show=True, show_observations=False)
input("Continue?")
def reset_renderer(self):
self.renderer = RenderTool(self.flatland_env,
gl="PILSVG",
agent_render_variant=AgentRenderVariant.
AGENT_SHOWS_OPTIONS_AND_BOX,
show_debug=True,
screen_height=700,
screen_width=1300)
def solve(env, width, height, naive, predictor):
env_renderer = RenderTool(env)
solver = r2_solver.Solver(1)
obs, _ = env.reset()
env.obs_builder.find_safe_edges(env)
predictor.env = env
predictor.get()
for step in range(100):
# print(obs)
# print(obs.shape)
if naive:
_action = naive_solver(env, obs)
else:
_action = solver.GetMoves(env.agents, obs)
obs_paths = TL_detector(env, obs, _action)
for k in obs_paths.keys():
if obs_paths[k] is not None and improved_solver(obs_paths[k]) == 0:
_action[k] = 4
for k in _action.keys():
if env.agents[k].position is None:
continue
pos = (env.agents[k].position[0], env.agents[k].position[1],
env.agents[k].direction)
if _action[k] != 0 and _action[
k] != 4 and pos in env.dev_pred_dict[k]:
env.dev_pred_dict[k].remove(pos)
next_obs, all_rewards, done, _ = env.step(_action)
print("Rewards: {}, [done={}]".format(all_rewards, done))
img = env_renderer.render_env(show=True,
show_inactive_agents=False,
show_predictions=True,
show_observations=False,
frames=True,
return_image=True)
cv2.imwrite("./env_images/" + str(step).zfill(3) + ".jpg", img)
obs = next_obs.copy()
if obs is None or done['__all__']:
break
unfinished_agents = []
for k in done.keys():
if not done[k] and type(k) is int:
unfinished_agents.append(k)
with open('observations_and_agents.pickle', 'wb') as f:
pickle.dump((env.obs_builder.obs_dict, unfinished_agents,
env.obs_builder.branches, env.obs_builder.safe_map), f)
return
def render_env(env):
env_renderer = RenderTool(env, gl="PGL")
env_renderer.render_env()
image = env_renderer.get_image()
pil_image = PIL.Image.fromarray(image)
#print("RENDER")
#pil_image.show()
images.append(pil_image)
print(len(images))
def initialize_renderer(self, mode="human"):
# Initiate the renderer
from flatland.utils.rendertools import RenderTool, AgentRenderVariant
self.renderer = RenderTool(
self,
gl="PGL", # gl="TKPILSVG",
agent_render_variant=AgentRenderVariant.ONE_STEP_BEHIND,
show_debug=False,
screen_height=600, # Adjust these parameters to fit your resolution
screen_width=800) # Adjust these parameters to fit your resolution
def test_shortest_path_predictor_conflicts(rendering=False):
rail, rail_map = make_invalid_simple_rail()
env = RailEnv(
width=rail_map.shape[1],
height=rail_map.shape[0],
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(),
number_of_agents=2,
obs_builder_object=TreeObsForRailEnv(
max_depth=2, predictor=ShortestPathPredictorForRailEnv()),
)
env.reset()
# set the initial position
agent = env.agents[0]
agent.initial_position = (5, 6) # south dead-end
agent.position = (5, 6) # south dead-end
agent.direction = 0 # north
agent.initial_direction = 0 # north
agent.target = (3, 9) # east dead-end
agent.moving = True
agent.status = RailAgentStatus.ACTIVE
agent = env.agents[1]
agent.initial_position = (3, 8) # east dead-end
agent.position = (3, 8) # east dead-end
agent.direction = 3 # west
agent.initial_direction = 3 # west
agent.target = (6, 6) # south dead-end
agent.moving = True
agent.status = RailAgentStatus.ACTIVE
observations, info = env.reset(False, False, True)
if rendering:
renderer = RenderTool(env, gl="PILSVG")
renderer.render_env(show=True, show_observations=False)
input("Continue?")
# get the trees to test
obs_builder: TreeObsForRailEnv = env.obs_builder
pp = pprint.PrettyPrinter(indent=4)
tree_0 = observations[0]
tree_1 = observations[1]
env.obs_builder.util_print_obs_subtree(tree_0)
env.obs_builder.util_print_obs_subtree(tree_1)
# check the expectations
expected_conflicts_0 = [('F', 'R')]
expected_conflicts_1 = [('F', 'L')]
_check_expected_conflicts(expected_conflicts_0, obs_builder, tree_0,
"agent[0]: ")
_check_expected_conflicts(expected_conflicts_1, obs_builder, tree_1,
"agent[1]: ")
class OurEnv(RailEnv):
def reset(self, *args, **kwargs):
return_val = super().reset(*args, **kwargs)
self.env_renderer = RenderTool(env)
self.step({0: RailEnvActions.MOVE_FORWARD})
return return_val
def step(self, *args, **kwargs):
self.env_renderer.render_env(show=True)
print(args[0])
observation, reward, done, info = super().step(*args, **kwargs)
return observation, reward, done["__all__"], info
def render(self, mode='human'):
# TODO: Merge both strategies (Jupyter vs .py)
# In .py files
# self.renderer.render_env(show=False, show_observations=False, show_predictions=False)
# In Jupyter Notebooks
env_renderer = RenderTool(self.flatland_env, gl="PILSVG")
env_renderer.render_env()
image = env_renderer.get_image()
pil_image = Image.fromarray(image)
display(pil_image)
return image
def render_test(parameters, test_nr=0, nr_examples=5):
for trial in range(nr_examples):
# Reset the env
print(
'Showing {} Level {} with (x_dim,y_dim) = ({},{}) and {} Agents.'.
format(test_nr, trial, parameters[0], parameters[1],
parameters[2]))
file_name = "./Tests/{}/Level_{}.pkl".format(test_nr, trial)
env = RailEnv(
width=1,
height=1,
rail_generator=rail_from_file(file_name),
obs_builder_object=TreeObsForRailEnv(max_depth=2),
number_of_agents=1,
)
env_renderer = RenderTool(
env,
gl="PILSVG",
)
env_renderer.set_new_rail()
env.reset(False, False)
env_renderer.render_env(show=True, show_observations=False)
time.sleep(0.1)
env_renderer.close_window()
return
def evaluate(n_episodes):
run = SUBMISSIONS["rlps-tcpr"]
config, run = init_run(run)
agent = ShortestPathRllibAgent(get_agent(config, run))
env = get_env(config, rl=True)
env_renderer = RenderTool(env, screen_width=8800)
returns = []
pcs = []
malfs = []
for _ in tqdm(range(n_episodes)):
obs, _ = env.reset(regenerate_schedule=True, regenerate_rail=True)
if RENDER:
env_renderer.reset()
env_renderer.render_env(show=True,
frames=True,
show_observations=False)
if not obs:
break
steps = 0
ep_return = 0
done = defaultdict(lambda: False)
robust_env = RobustFlatlandGymEnv(rail_env=env,
max_nr_active_agents=200,
observation_space=None,
priorizer=DistToTargetPriorizer(),
allow_noop=True)
sorted_handles = robust_env.priorizer.priorize(handles=list(
obs.keys()),
rail_env=env)
while not done['__all__']:
actions = agent.compute_actions(obs, env)
robust_actions = robust_env.get_robust_actions(
actions, sorted_handles)
obs, all_rewards, done, info = env.step(robust_actions)
if RENDER:
env_renderer.render_env(show=True,
frames=True,
show_observations=False)
print('.', end='', flush=True)
steps += 1
ep_return += np.sum(list(all_rewards.values()))
pc = np.sum(np.array([1 for a in env.agents if is_done(a)
])) / env.get_num_agents()
print("EPISODE PC:", pc)
n_episodes += 1
pcs.append(pc)
returns.append(ep_return /
(env._max_episode_steps * env.get_num_agents()))
malfs.append(
np.sum([a.malfunction_data['nr_malfunctions']
for a in env.agents]))
return pcs, returns, malfs
def render(self, **kwargs):
from flatland.utils.rendertools import RenderTool
if not self.env_renderer:
self.env_renderer = RenderTool(self.env, gl="PILSVG")
self.env_renderer.set_new_rail()
self.env_renderer.render_env(show=True,
frames=False,
show_observations=False,
**kwargs)
time.sleep(0.1)
self.env_renderer.render_env(show=True,
frames=False,
show_observations=False,
**kwargs)
return self.env_renderer.get_image()
def check_path(env,
rail,
position,
direction,
target,
expected,
rendering=False):
agent = env.agents[0]
agent.position = position # south dead-end
agent.direction = direction # north
agent.target = target # east dead-end
agent.moving = True
if rendering:
renderer = RenderTool(env, gl="PILSVG")
renderer.render_env(show=True, show_observations=False)
input("Continue?")
assert rail.check_path_exists(agent.position, agent.direction,
agent.target) == expected
def get_renderer(self):
'''
Return a renderer for the current environment
'''
return RenderTool(self,
agent_render_variant=AgentRenderVariant.
AGENT_SHOWS_OPTIONS_AND_BOX,
show_debug=True,
screen_height=1080,
screen_width=1920)
def env_renderer(self, env):
# RailEnv.DEPOT_POSITION = lambda agent, agent_handle : (agent_handle % env.height,0)
# To show the screen
env_renderer = RenderTool(env,
gl="PILSVG",
agent_render_variant=AgentRenderVariant.
AGENT_SHOWS_OPTIONS_AND_BOX,
show_debug=True,
screen_height=800,
screen_width=800)
return env_renderer
def createEnvSet(nStart, nEnd, sDir, bSmall=True):
# print("Generate small envs in train-envs-small:")
print(f"Generate envs (small={bSmall}) in dir {sDir}:")
sDirImages = "train-envs-small/images/"
if not os.path.exists(sDirImages):
os.makedirs(sDirImages)
for test_id in range(nStart, nEnd, 1):
env = create_test_env(RandomTestParams_small, test_id, sDir)
oRender = RenderTool(env, gl="PILSVG")
# oRender.envs = envs
# oRender.set_new_rail()
oRender.render_env()
g2img = oRender.get_image()
imgPIL = Image.fromarray(g2img)
# imgPIL.show()
imgPIL.save(sDirImages + "Level_{}.png".format(test_id))
def replay_verify(max_episode_steps: int, ctl: ControllerFromTrainRuns,
env: RailEnv, rendering: bool):
"""Replays this deterministic `ActionPlan` and verifies whether it is feasible."""
if rendering:
renderer = RenderTool(env,
gl="PILSVG",
agent_render_variant=AgentRenderVariant.
AGENT_SHOWS_OPTIONS_AND_BOX,
show_debug=True,
clear_debug_text=True,
screen_height=1000,
screen_width=1000)
renderer.render_env(show=True,
show_observations=False,
show_predictions=False)
i = 0
while not env.dones['__all__'] and i <= max_episode_steps:
for agent_id, agent in enumerate(env.agents):
way_point: WayPoint = ctl.get_way_point_before_or_at_step(
agent_id, i)
assert agent.position == way_point.position, \
"before {}, agent {} at {}, expected {}".format(i, agent_id, agent.position,
way_point.position)
actions = ctl.act(i)
print("actions for {}: {}".format(i, actions))
obs, all_rewards, done, _ = env.step(actions)
if rendering:
renderer.render_env(show=True,
show_observations=False,
show_predictions=False)
i += 1
def evaluate(n_episodes):
run = SUBMISSIONS["ato"]
config, run = init_run(run)
agent = get_agent(config, run)
env = get_env(config, rl=True)
env_renderer = RenderTool(env, screen_width=8800)
returns = []
pcs = []
malfs = []
for _ in tqdm(range(n_episodes)):
obs, _ = env.reset(regenerate_schedule=True, regenerate_rail=True)
if RENDER:
env_renderer.reset()
env_renderer.render_env(show=True,
frames=True,
show_observations=False)
if not obs:
break
steps = 0
ep_return = 0
done = defaultdict(lambda: False)
while not done['__all__']:
actions = agent.compute_actions(obs, explore=False)
obs, all_rewards, done, info = env.step(actions)
if RENDER:
env_renderer.render_env(show=True,
frames=True,
show_observations=False)
print('.', end='', flush=True)
steps += 1
ep_return += np.sum(list(all_rewards.values()))
pc = np.sum(np.array([1 for a in env.agents if is_done(a)
])) / env.get_num_agents()
print("EPISODE PC:", pc)
n_episodes += 1
pcs.append(pc)
returns.append(ep_return /
(env._max_episode_steps * env.get_num_agents()))
malfs.append(
np.sum([a.malfunction_data['nr_malfunctions']
for a in env.agents]))
return pcs, returns, malfs
def __init__(self,
n_cars=3,
n_acts=5,
min_obs=-1,
max_obs=1,
n_nodes=2,
ob_radius=10,
x_dim=36,
y_dim=36,
feats='all'):
self.tree_obs = tree_observation.TreeObservation(n_nodes)
self.n_cars = n_cars
self.n_nodes = n_nodes
self.ob_radius = ob_radius
self.feats = feats
rail_gen = sparse_rail_generator(max_num_cities=3,
seed=666,
grid_mode=False,
max_rails_between_cities=2,
max_rails_in_city=3)
self._rail_env = RailEnv(
width=x_dim,
height=y_dim,
rail_generator=rail_gen,
schedule_generator=sparse_schedule_generator(speed_ration_map),
number_of_agents=n_cars,
malfunction_generator_and_process_data=malfunction_from_params(
stochastic_data),
obs_builder_object=self.tree_obs)
self.renderer = RenderTool(self._rail_env, gl="PILSVG")
self.action_dict = dict()
self.info = dict()
self.old_obs = dict()
def main(args):
try:
opts, args = getopt.getopt(args, "", ["sleep-for-animation=", ""])
except getopt.GetoptError as err:
print(str(err)) # will print something like "option -a not recognized"
sys.exit(2)
sleep_for_animation = True
for o, a in opts:
if o in ("--sleep-for-animation"):
sleep_for_animation = str2bool(a)
else:
assert False, "unhandled option"
# Initiate the Predictor
custom_predictor = ShortestPathPredictorForRailEnv(10)
# Pass the Predictor to the observation builder
custom_obs_builder = ObservePredictions(custom_predictor)
# Initiate Environment
env = RailEnv(width=10,
height=10,
rail_generator=complex_rail_generator(nr_start_goal=5,
nr_extra=1,
min_dist=8,
max_dist=99999,
seed=1),
schedule_generator=complex_schedule_generator(),
number_of_agents=3,
obs_builder_object=custom_obs_builder)
obs, info = env.reset()
env_renderer = RenderTool(env, gl="PILSVG")
# We render the initial step and show the obsered cells as colored boxes
env_renderer.render_env(show=True,
frames=True,
show_observations=True,
show_predictions=False)
action_dict = {}
for step in range(100):
for a in range(env.get_num_agents()):
action = np.random.randint(0, 5)
action_dict[a] = action
obs, all_rewards, done, _ = env.step(action_dict)
print("Rewards: ", all_rewards, " [done=", done, "]")
env_renderer.render_env(show=True,
frames=True,
show_observations=True,
show_predictions=False)
if sleep_for_animation:
time.sleep(0.5)
def env_gradual_update(input_env, agent=False, hardness_lvl=1):
agent_num = input_env.number_of_agents
env_width = input_env.width + 4
env_height = input_env.height + 4
map_agent_ratio = int(np.round(((env_width + env_height) / 2) / 5 - 2))
if map_agent_ratio > 0:
agent_num = int(np.round(((env_width + env_height) / 2) / 5 - 2))
else:
agent_num = 1
if hardness_lvl == 1:
rail_generator = complex_rail_generator(nr_start_goal=20,
nr_extra=1,
min_dist=9,
max_dist=99999,
seed=0)
schedule_generator = complex_schedule_generator()
else:
rail_generator = sparse_rail_generator(nr_start_goal=9,
nr_extra=1,
min_dist=9,
max_dist=99999,
seed=0)
schedule_generator = sparse_schedule_generator()
global env, env_renderer, render
if render:
env_renderer.close_window()
env = RailEnv(width=env_width,
height=env_height,
rail_generator=rail_generator,
schedule_generator=schedule_generator,
obs_builder_object=GlobalObsForRailEnv(),
number_of_agents=agent_num)
env_renderer = RenderTool(env)
def create_env(nr_start_goal=10,
nr_extra=2,
min_dist=8,
max_dist=99999,
nr_agent=10,
seed=0,
render_mode='PIL'):
env = RailEnv(width=30,
height=30,
rail_generator=complex_rail_generator(
nr_start_goal, nr_extra, min_dist, max_dist, seed),
schedule_generator=complex_schedule_generator(),
obs_builder_object=GlobalObsForRailEnv(),
number_of_agents=nr_agent)
env_renderer = RenderTool(env, gl=render_mode)
obs = env.reset()
return env, env_renderer, obs
def main(args):
try:
opts, args = getopt.getopt(args, "", ["sleep-for-animation=", ""])
except getopt.GetoptError as err:
print(str(err)) # will print something like "option -a not recognized"
sys.exit(2)
sleep_for_animation = True
for o, a in opts:
if o in ("--sleep-for-animation"):
sleep_for_animation = str2bool(a)
else:
assert False, "unhandled option"
env = RailEnv(width=7,
height=7,
rail_generator=complex_rail_generator(nr_start_goal=10,
nr_extra=1,
min_dist=5,
max_dist=99999,
seed=1),
schedule_generator=complex_schedule_generator(),
number_of_agents=1,
obs_builder_object=SingleAgentNavigationObs())
obs, info = env.reset()
env_renderer = RenderTool(env)
env_renderer.render_env(show=True, frames=True, show_observations=True)
for step in range(100):
action = np.argmax(obs[0]) + 1
obs, all_rewards, done, _ = env.step({0: action})
print("Rewards: ", all_rewards, " [done=", done, "]")
env_renderer.render_env(show=True, frames=True, show_observations=True)
if sleep_for_animation:
time.sleep(0.1)
if done["__all__"]:
break
env_renderer.close_window()
def env_random_update(input_env, decay, agent=False, hardness_lvl=1):
agent_num = np.random.randint(1, 5)
env_width = (agent_num + 2) * 5
env_height = (agent_num + 2) * 5
if hardness_lvl == 1:
rail_generator = complex_rail_generator(nr_start_goal=20,
nr_extra=1,
min_dist=9,
max_dist=99999,
seed=0)
schedule_generator = complex_schedule_generator()
else:
rail_generator = sparse_rail_generator(nr_start_goal=9,
nr_extra=1,
min_dist=9,
max_dist=99999,
seed=0)
schedule_generator = sparse_schedule_generator()
global env, env_renderer, render
if render:
env_renderer.close_window()
env = RailEnv(width=env_width,
height=env_height,
rail_generator=rail_generator,
schedule_generator=schedule_generator,
obs_builder_object=GlobalObsForRailEnv(),
number_of_agents=agent_num)
env_renderer = RenderTool(env)
max_num_cities=n_cities,
grid_mode=False,
max_rails_between_cities=max_rails_between_cities,
max_rails_in_city=max_rails_in_city),
schedule_generator=sparse_schedule_generator(speed_profiles),
number_of_agents=n_agents,
malfunction_generator_and_process_data=malfunction_from_params(
malfunction_parameters),
obs_builder_object=tree_observation,
random_seed=seed)
env.reset(regenerate_schedule=True, regenerate_rail=True)
# Setup renderer
env_renderer = RenderTool(env)
'''
env_renderer.render_env(show=True,show_predictions=False)
time.sleep(5)
env_renderer.close_window()
'''
n_features_per_node = env.obs_builder.observation_dim
n_nodes = 0
for i in range(observation_tree_depth + 1):
n_nodes += np.power(4, i)
state_size = n_features_per_node * n_nodes
action_size = 5
# Max number of steps per episode
# This is the official formula used during evaluations
env = RailEnv(width=x_dim,
height=y_dim,
rail_generator=sparse_rail_generator(max_num_cities=3,
# Number of cities in map (where train stations are)
seed=1, # Random seed
grid_mode=False,
max_rails_between_cities=2,
max_rails_in_city=4),
schedule_generator=sparse_schedule_generator(speed_ration_map),
number_of_agents=n_agents,
malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
obs_builder_object=TreeObservation)
env.reset()
env_renderer = RenderTool(env, gl="PILSVG", )
num_features_per_node = env.obs_builder.observation_dim
tree_depth = 2
nr_nodes = 0
for i in range(tree_depth + 1):
nr_nodes += np.power(4, i)
state_size = num_features_per_node * nr_nodes
action_size = 5
# We set the number of episodes we would like to train on
if 'n_trials' not in locals():
n_trials = 60000
max_steps = int(3 * (env.height + env.width))
eps = 1.
eps_end = 0.005
def main(args, dir):
'''
:param args:
:return:
Episodes to debug (set breakpoint in episodes loop to debug):
- ep = 3, agent 1 spawns in front of 3, blocking its path; 0 and 2 are in a deadlock since they have same priority
- ep = 4, agents stop because of wrong priorities even though the conflict zone wasn't entered,
- ep = 14,
'''
rail_generator = sparse_rail_generator(
max_num_cities=args.max_num_cities,
seed=args.seed,
grid_mode=args.grid_mode,
max_rails_between_cities=args.max_rails_between_cities,
max_rails_in_city=args.max_rails_in_city,
)
# Maps speeds to % of appearance in the env
speed_ration_map = {
1.: 0.25, # Fast passenger train
1. / 2.: 0.25, # Fast freight train
1. / 3.: 0.25, # Slow commuter train
1. / 4.: 0.25
} # Slow freight train
observation_builder = GraphObsForRailEnv(
predictor=ShortestPathPredictorForRailEnv(
max_depth=args.prediction_depth),
bfs_depth=4)
env = RailEnv(
width=args.width,
height=args.height,
rail_generator=rail_generator,
schedule_generator=sparse_schedule_generator(speed_ration_map),
number_of_agents=args.num_agents,
obs_builder_object=observation_builder,
malfunction_generator_and_process_data=malfunction_from_params(
parameters={
'malfunction_rate':
args.malfunction_rate, # Rate of malfunction occurrence
'min_duration':
args.min_duration, # Minimal duration of malfunction
'max_duration':
args.max_duration # Max duration of malfunction
}))
if args.render:
env_renderer = RenderTool(env,
agent_render_variant=AgentRenderVariant.
AGENT_SHOWS_OPTIONS_AND_BOX,
show_debug=True)
sm = stateMachine()
tb = TestBattery(env, observation_builder)
state_machine_action_dict = {}
railenv_action_dict = {}
# max_time_steps = env.compute_max_episode_steps(args.width, args.height)
max_time_steps = 200
T_rewards = [] # List of episodes rewards
T_Qs = [] # List of q values
T_num_done_agents = [] # List of number of done agents for each episode
T_all_done = [] # If all agents completed in each episode
T_episodes = [] # Time taken for each episode
if args.save_image and not os.path.isdir("image_dump"):
os.makedirs("image_dump")
step_taken = 0
total_step_taken = 0
total_episodes = 0
step_times = [] # Time taken for each step
for ep in range(args.num_episodes):
# Reset info at the beginning of an episode
start_time = time.time() # Take time of one episode
if args.generate_baseline:
if not os.path.isdir("image_dump/" + str(dir)) and args.save_image:
os.makedirs("image_dump/" + str(dir))
else:
if not os.path.isdir("image_dump/" + str(ep)) and args.save_image:
os.makedirs("image_dump/" + str(ep))
state, info = env.reset()
tb.reset()
if args.render:
env_renderer.reset()
reward_sum, all_done = 0, False # reward_sum contains the cumulative reward obtained as sum during the steps
num_done_agents = 0
state_machine_action = {}
for i in range(env.number_of_agents):
state_machine_action[i] = 0
for step in range(max_time_steps):
start_step_time = time.time()
#if step % 10 == 0:
# print(step)
# Test battery
# see test_battery.py
triggers = tb.tests(state, args.prediction_depth,
state_machine_action)
# state machine based on triggers of test battery
# see state_machine.py
state_machine_action = sm.act(
triggers) # State machine picks action
for a in range(env.get_num_agents()):
#if info['action_required'][a]:
# #railenv_action = observation_builder.choose_railenv_action(a, state_machine_action[a])
# railenv_action = observation_builder.choose_railenv_action(a, state_machine_action[a])
# state_machine_action_dict.update({a: state_machine_action})
# railenv_action_dict.update({a: railenv_action})
# railenv_action = observation_builder.choose_railenv_action(a, state_machine_action[a])
railenv_action = observation_builder.choose_railenv_action(
a, state_machine_action[a])
state_machine_action_dict.update({a: state_machine_action})
railenv_action_dict.update({a: railenv_action})
state, reward, done, info = env.step(
railenv_action_dict) # Env step
if args.generate_baseline:
#env_renderer.render_env(show=True, show_observations=False, show_predictions=True)
env_renderer.render_env(show=False,
show_observations=False,
show_predictions=True)
else:
env_renderer.render_env(show=True,
show_observations=False,
show_predictions=True)
if args.generate_baseline:
if args.save_image:
env_renderer.save_image("image_dump/" + str(dir) +
"/image_" + str(step) + "_.png")
else:
if args.save_image:
env_renderer.save_image("image_dump/" + str(ep) +
"/image_" + str(step) + "_.png")
if args.debug:
for a in range(env.get_num_agents()):
log('\n\n#########################################')
log('\nInfo for agent {}'.format(a))
#log('\npath : {}'.format(state[a]["path"]))
log('\noverlap : {}'.format(state[a]["overlap"]))
log('\ndirection : {}'.format(state[a]["direction"]))
log('\nOccupancy, first layer: {}'.format(
state[a]["occupancy"]))
log('\nOccupancy, second layer: {}'.format(
state[a]["conflict"]))
log('\nForks: {}'.format(state[a]["forks"]))
log('\nTarget: {}'.format(state[a]["target"]))
log('\nPriority: {}'.format(state[a]["priority"]))
log('\nMax priority encountered: {}'.format(
state[a]["max_priority"]))
log('\nNum malfunctioning agents (globally): {}'.format(
state[a]["n_malfunction"]))
log('\nNum agents ready to depart (globally): {}'.format(
state[a]["ready_to_depart"]))
log('\nStatus: {}'.format(info['status'][a]))
log('\nPosition: {}'.format(env.agents[a].position))
log('\nTarget: {}'.format(env.agents[a].target))
log('\nMoving? {} at speed: {}'.format(
env.agents[a].moving, info['speed'][a]))
log('\nAction required? {}'.format(
info['action_required'][a]))
log('\nState machine action: {}'.format(
state_machine_action_dict[a]))
log('\nRailenv action: {}'.format(railenv_action_dict[a]))
log('\nRewards: {}'.format(reward[a]))
log('\n\n#########################################')
reward_sum += sum(reward[a] for a in range(env.get_num_agents()))
step_taken = step
time_taken_step = time.time() - start_step_time
step_times.append(time_taken_step)
if done['__all__']:
all_done = True
break
total_step_taken += step_taken
time_taken = time.time() - start_time # Time taken for one episode
total_episodes = ep
# Time metrics - too precise
avg_time_step = sum(step_times) / step_taken
#print("Avg time step: " + str(avg_time_step))
# No need to close the renderer since env parameter sizes stay the same
T_rewards.append(reward_sum)
# Compute num of agents that reached their target
for a in range(env.get_num_agents()):
if done[a]:
num_done_agents += 1
percentage_done_agents = num_done_agents / env.get_num_agents()
log("\nDone agents in episode: {}".format(percentage_done_agents))
T_num_done_agents.append(
percentage_done_agents) # In proportion to total
T_all_done.append(all_done)
# Average number of agents that reached their target
avg_done_agents = sum(T_num_done_agents) / len(T_num_done_agents) if len(
T_num_done_agents) > 0 else 0
avg_reward = sum(T_rewards) / len(T_rewards) if len(T_rewards) > 0 else 0
avg_norm_reward = avg_reward / (max_time_steps / env.get_num_agents())
avg_ep_time = sum(T_episodes) / args.num_episodes
if total_episodes == 0:
total_episodes = 1
log("\nSeed: " + str(args.seed) \
+ "\t | Avg_done_agents: " + str(avg_done_agents)\
+ "\t | Avg_reward: " + str(avg_reward)\
+ "\t | Avg_norm_reward: " + str(avg_norm_reward)\
+ "\t | Max_num_time_steps: " + str(max_time_steps)\
+ "\t | Avg_num_time_steps: " + str(total_step_taken/total_episodes)
+ "\t | Avg episode time: " + str(avg_ep_time))
rail_generator=rail_generator,
schedule_generator=schedule_generator,
number_of_agents=nr_trains,
obs_builder_object=observation_builder,
malfunction_generator_and_process_data=malfunction_from_params(
stochastic_data),
remove_agents_at_target=True)
observations, information = local_env.reset()
# print("observations:", observations)
# Initiate the renderer
env_renderer = RenderTool(
local_env,
gl="PILSVG",
agent_render_variant=AgentRenderVariant.ONE_STEP_BEHIND,
show_debug=False,
screen_height=1200, # Adjust these parameters to fit your resolution
screen_width=1800) # Adjust these parameters to fit your resolution
######### Get arguments of the script #########
parser = argparse.ArgumentParser()
parser.add_argument("-step", type=int, help="steps")
args = parser.parse_args()
######### Custom controller: imports #########
controller = GreedyAgent(218, local_env.action_space[0])
######### Define custom controller #########
def my_controller(obs, number_of_agents, astar_paths_readable, timestamp):
