run.py 5.39 KB
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from flatland.evaluators.client import FlatlandRemoteClient
from flatland.envs.observations import TreeObsForRailEnv
from flatland.envs.predictions import ShortestPathPredictorForRailEnv
import numpy as np



#####################################################################
# Instantiate a Remote Client
#####################################################################
remote_client = FlatlandRemoteClient()

#####################################################################
# Define your custom controller
#
# which can take an observation, and the number of agents and 
# compute the necessary action for this step for all (or even some)
# of the agents
#####################################################################
def my_controller(obs, number_of_agents):
    _action = {}
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    for _idx in range(number_of_agents):
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        _action[_idx] = np.random.randint(0, 5)
    return _action

#####################################################################
# Instantiate your custom Observation Builder
# 
# You can build your own Observation Builder by following 
# the example here : 
# https://gitlab.aicrowd.com/flatland/flatland/blob/master/flatland/envs/observations.py#L14
#####################################################################
my_observation_builder = TreeObsForRailEnv(
                                max_depth=3,
                                predictor=ShortestPathPredictorForRailEnv()
                            )

#####################################################################
# Main evaluation loop
#
# This iterates over an arbitrary number of env evaluations
#####################################################################
evaluation_number = 0
while True:

    evaluation_number += 1
    # Switch to a new evaluation environemnt
    # 
    # a remote_client.env_create is similar to instantiating a 
    # RailEnv and then doing a env.reset()
    # hence it returns the first observation from the 
    # env.reset()
    # 
    # You can also pass your custom observation_builder object
    # to allow you to have as much control as you wish 
    # over the observation of your choice.
    observation = remote_client.env_create(
                    obs_builder_object=my_observation_builder
                )
    if not observation:
        #
        # If the remote_client returns False on a `env_create` call,
        # then it basically means that your agent has already been 
        # evaluated on all the required evaluation environments,
        # and hence its safe to break out of the main evaluation loop
        break
    
    print("Evaluation Number : {}".format(evaluation_number))

    #####################################################################
    # Access to a local copy of the environment
    # 
    #####################################################################
    # Note: You can access a local copy of the environment 
    # by using : 
    #       remote_client.env 
    # 
    # But please ensure to not make any changes (or perform any action) on 
    # the local copy of the env, as then it will diverge from 
    # the state of the remote copy of the env, and the observations and 
    # rewards, etc will behave unexpectedly
    # 
    # You can however probe the local_env instance to get any information
    # you need from the environment. It is a valid RailEnv instance.
    local_env = remote_client.env
    number_of_agents = len(local_env.agents)

    # Now we enter into another infinite loop where we 
    # compute the actions for all the individual steps in this episode
    # until the episode is `done`
    # 
    # An episode is considered done when either all the agents have 
    # reached their target destination
    # or when the number of time steps has exceed max_time_steps, which 
    # is defined by : 
    #
    # max_time_steps = int(1.5 * (env.width + env.height))
    #
    while True:
        #####################################################################
        # Evaluation of a single episode
        #
        #####################################################################
        # Compute the action for this step by using the previously 
        # defined controlle
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        action = my_controller(observation, number_of_agents)
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        # Perform the chosen action on the environment.
        # The action gets applied to both the local and the remote copy 
        # of the environment instance, and the observation is what is 
        # returned by the local copy of the env, and the rewards, and done and info
        # are returned by the remote copy of the env
        observation, all_rewards, done, info = remote_client.env_step(action)
        if done['__all__']:
            print("Reward : ", sum(list(all_rewards.values())))
            #
            # When done['__all__'] == True, then the evaluation of this 
            # particular Env instantiation is complete, and we can break out 
            # of this loop, and move onto the next Env evaluation
            break

print("Evaluation of all environments complete...")
########################################################################
# Submit your Results
# 
# Please do not forget to include this call, as this triggers the 
# final computation of the score statistics, video generation, etc
# and is necesaary to have your submission marked as successfully evaluated
########################################################################
print(remote_client.submit())