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 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 evaluate(n_episodes, rl_prio=True):
agent = None
if rl_prio:
config, run = init_run()
agent = get_agent(config, run)
env = get_env(config, rl=True)
else:
env = get_env(rl=False)
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 = CprFlatlandGymEnv(rail_env=env,
max_nr_active_agents=200,
observation_space=None,
priorizer=NrAgentsSameStart(),
allow_noop=True)
# if rl_prio:
# priorities = prio_agent.compute_actions(obs, explore=False)
# sorted_actions = {k: v for k, v in sorted(priorities.items(), key=lambda item: item[1], reverse=True)}
# sorted_handles = list(sorted_actions.keys())
# else:
sorted_handles = robust_env.priorizer.priorize(handles=list(
obs.keys()),
rail_env=env)
while not done['__all__']:
actions = ShortestPathAgent().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
if (time() - start_time) > TIME_LIMIT:
skip(done)
break
if done['__all__']:
total_reward = episode_end_info(
all_rewards,
total_reward,
evaluation_number,
steps,
remote_client=remote_client)
break
except TimeoutException as err:
print(
"Timeout! Will skip this episode and go to the next.",
err)
break
except TimeoutException as err:
print(
"Timeout during planning time. Will skip to next evaluation!",
err)
print("Evaluation of all environments complete...")
print(remote_client.submit())
if __name__ == "__main__":
config, run = init_run()
evaluate(config, run)