random_agent.py

import numpy as np
from flatland.envs.observations import GlobalObsForRailEnv
from flatland.evaluators.client import FlatlandRemoteClient

remote_client = FlatlandRemoteClient()


def my_controller(obs, _env):
  _action = {}
  for _idx, _ in enumerate(_env.agents):
    _action[_idx] = np.random.randint(0, 5)
  return _action


my_observation_builder = GlobalObsForRailEnv()

episode = 0

while True:
  print("/ start random_agent", flush=True)
  print("==============")
  episode += 1
  print("[INFO] EPISODE_START : {}".format(episode))
  # NO WAY TO CHECK service/self.evaluation_done in client

  obs, info = remote_client.env_create(obs_builder_object=my_observation_builder)
  if not obs:
    """
    The remote env returns False as the first obs
    when it is done evaluating all the individual episodes
    """
    print("[INFO] DONE ALL, BREAKING")
    break

  while True:
    action = my_controller(obs, remote_client.env)
    try:
      observation, all_rewards, done, info = remote_client.env_step(
        action)
    except:
      print("[ERR] DONE BUT step() CALLED")

    if (True):  # debug
      print("-----")
      # print(done)
      print("[DEBUG] REW: ", all_rewards)
    # break
    if done['__all__']:
      print("[INFO] EPISODE_DONE : ", episode)
      print("[INFO] TOTAL_REW: ", sum(list(all_rewards.values())))
      break

print("Evaluation Complete...")
print(remote_client.submit())
print("\\ end random_agent", flush=True)