run.py

import sys
import time
from argparse import Namespace
from pathlib import Path

import numpy as np
from flatland.core.env_observation_builder import DummyObservationBuilder
from flatland.envs.predictions import ShortestPathPredictorForRailEnv
from flatland.envs.rail_env import RailEnvActions
from flatland.evaluators.client import FlatlandRemoteClient
from flatland.evaluators.client import TimeoutException

from utils.dead_lock_avoidance_agent import DeadLockAvoidanceAgent
from utils.deadlock_check import check_if_all_blocked
from utils.fast_tree_obs import FastTreeObs

base_dir = Path(__file__).resolve().parent.parent
sys.path.append(str(base_dir))

from reinforcement_learning.dddqn_policy import DDDQNPolicy

####################################################
# EVALUATION PARAMETERS

# Print per-step logs
VERBOSE = True

# Checkpoint to use (remember to push it!)
# checkpoint = "./checkpoints/201112143850-5400.pth" # 21.220418678677177 DEPTH=2 AGENTS=10
checkpoint = "./checkpoints/201113211844-6700.pth" # 19.690047767961005 DEPTH=2 AGENTS=20


# Use last action cache
USE_ACTION_CACHE = False
USE_DEAD_LOCK_AVOIDANCE_AGENT = False

# Observation parameters (must match training parameters!)
observation_tree_depth = 2
observation_radius = 10
observation_max_path_depth = 30

####################################################

remote_client = FlatlandRemoteClient()

# Observation builder
predictor = ShortestPathPredictorForRailEnv(observation_max_path_depth)
tree_observation = FastTreeObs(max_depth=observation_tree_depth)

# Calculates state and action sizes
state_size = tree_observation.observation_dim
action_size = 5

# Creates the policy. No GPU on evaluation server.
policy = DDDQNPolicy(state_size, action_size, Namespace(**{'use_gpu': False}), evaluation_mode=True)
# policy = PPOAgent(state_size, action_size, 10)
policy.load(checkpoint)

#####################################################################
# Main evaluation loop
#####################################################################
evaluation_number = 0

while True:
    evaluation_number += 1

    # We use a dummy observation and call TreeObsForRailEnv ourselves when needed.
    # This way we decide if we want to calculate the observations or not instead
    # of having them calculated every time we perform an env step.
    time_start = time.time()
    observation, info = remote_client.env_create(
        obs_builder_object=DummyObservationBuilder()
    )
    env_creation_time = time.time() - time_start

    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 it's safe to break out of the main evaluation loop.
        break

    print("Env Path : ", remote_client.current_env_path)
    print("Env Creation Time : ", env_creation_time)

    local_env = remote_client.env
    nb_agents = len(local_env.agents)
    max_nb_steps = local_env._max_episode_steps

    tree_observation.set_env(local_env)
    tree_observation.reset()
    observation = tree_observation.get_many(list(range(nb_agents)))

    print("Evaluation {}: {} agents in {}x{}".format(evaluation_number, nb_agents, local_env.width, local_env.height))

    # 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`
    steps = 0

    # Bookkeeping
    time_taken_by_controller = []
    time_taken_per_step = []

    # Action cache: keep track of last observation to avoid running the same inferrence multiple times.
    # This only makes sense for deterministic policies.
    agent_last_obs = {}
    agent_last_action = {}
    nb_hit = 0

    if USE_DEAD_LOCK_AVOIDANCE_AGENT:
        policy = DeadLockAvoidanceAgent(local_env)

    while True:
        try:
            #####################################################################
            # Evaluation of a single episode
            #####################################################################
            steps += 1
            obs_time, agent_time, step_time = 0.0, 0.0, 0.0
            no_ops_mode = False

            if not check_if_all_blocked(env=local_env):
                time_start = time.time()
                action_dict = {}
                policy.start_step()
                if USE_DEAD_LOCK_AVOIDANCE_AGENT:
                    observation = np.zeros((local_env.get_num_agents(), 2))
                for agent in range(nb_agents):

                    if USE_DEAD_LOCK_AVOIDANCE_AGENT:
                        observation[agent][0] = agent
                        observation[agent][1] = steps

                    if info['action_required'][agent]:
                        if agent in agent_last_obs and np.all(agent_last_obs[agent] == observation[agent]):
                            # cache hit
                            action = agent_last_action[agent]
                            nb_hit += 1
                        else:
                            action = policy.act(observation[agent], eps=0.01)

                        action_dict[agent] = action

                        if USE_ACTION_CACHE:
                            agent_last_obs[agent] = observation[agent]
                            agent_last_action[agent] = action

                policy.end_step()
                agent_time = time.time() - time_start
                time_taken_by_controller.append(agent_time)

                time_start = time.time()
                _, all_rewards, done, info = remote_client.env_step(action_dict)
                step_time = time.time() - time_start
                time_taken_per_step.append(step_time)

                time_start = time.time()
                observation = tree_observation.get_many(list(range(nb_agents)))
                obs_time = time.time() - time_start

            else:
                # Fully deadlocked: perform no-ops
                no_ops_mode = True

                time_start = time.time()
                _, all_rewards, done, info = remote_client.env_step({})
                step_time = time.time() - time_start
                time_taken_per_step.append(step_time)

            nb_agents_done = sum(done[idx] for idx in local_env.get_agent_handles())

            if VERBOSE or done['__all__']:
                print(
                    "Step {}/{}\tAgents done: {}\t Obs time {:.3f}s\t Inference time {:.5f}s\t Step time {:.3f}s\t Cache hits {}\t No-ops? {}".format(
                        str(steps).zfill(4),
                        max_nb_steps,
                        nb_agents_done,
                        obs_time,
                        agent_time,
                        step_time,
                        nb_hit,
                        no_ops_mode
                    ), end="\r")

            if done['__all__']:
                # 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
                print()
                break

        except TimeoutException as err:
            # A timeout occurs, won't get any reward for this episode :-(
            # Skip to next episode as further actions in this one will be ignored.
            # The whole evaluation will be stopped if there are 10 consecutive timeouts.
            print("Timeout! Will skip this episode and go to the next.", err)
            break

    np_time_taken_by_controller = np.array(time_taken_by_controller)
    np_time_taken_per_step = np.array(time_taken_per_step)
    print("Mean/Std of Time taken by Controller : ", np_time_taken_by_controller.mean(),
          np_time_taken_by_controller.std())
    print("Mean/Std of Time per Step : ", np_time_taken_per_step.mean(), np_time_taken_per_step.std())
    print("=" * 100)

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 necessary to have your submission marked as successfully evaluated
########################################################################
print(remote_client.submit())