run.py

'''
DDDQNPolicy experiments - EPSILON impact analysis
----------------------------------------------------------------------------------------
checkpoint = "./checkpoints/201124171810-7800.pth"  # Training on AGENTS=10 with Depth=2
EPSILON = 0.000 # Sum Normalized Reward :  0.000000000000000 (primary score)
EPSILON = 0.002 # Sum Normalized Reward : 18.445875081269286 (primary score)
EPSILON = 0.005 # Sum Normalized Reward : 18.371733625865854 (primary score)
EPSILON = 0.010 # Sum Normalized Reward : 18.249244799876152 (primary score)
EPSILON = 0.020 # Sum Normalized Reward : 17.526987022691376 (primary score)
EPSILON = 0.030 # Sum Normalized Reward : 16.796885571003942 (primary score)
EPSILON = 0.040 # Sum Normalized Reward : 17.280787151431426 (primary score)
EPSILON = 0.050 # Sum Normalized Reward : 16.256945636647025 (primary score)
EPSILON = 0.100 # Sum Normalized Reward : 14.828347241759966 (primary score)
EPSILON = 0.200 # Sum Normalized Reward : 11.192330074898457 (primary score)
EPSILON = 0.300 # Sum Normalized Reward : 14.523067754608782 (primary score)
EPSILON = 0.400 # Sum Normalized Reward : 12.901508220410834 (primary score)
EPSILON = 0.500 # Sum Normalized Reward :  3.754660231871272 (primary score)
EPSILON = 1.000 # Sum Normalized Reward :  1.397180159192391 (primary score)
'''

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.observations import TreeObsForRailEnv
from flatland.envs.predictions import ShortestPathPredictorForRailEnv
from flatland.evaluators.client import FlatlandRemoteClient
from flatland.evaluators.client import TimeoutException

from reinforcement_learning.ppo_agent import PPOAgent
from utils.dead_lock_avoidance_agent import DeadLockAvoidanceAgent
from utils.deadlock_check import check_if_all_blocked
from utils.fast_tree_obs import FastTreeObs
from utils.observation_utils import normalize_observation

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
USE_FAST_TREEOBS = True
USE_PPO_AGENT = True

# Checkpoint to use (remember to push it!)
checkpoint = "./checkpoints/201124171810-7800.pth"  # 18.249244799876152 DEPTH=2 AGENTS=10
# checkpoint = "./checkpoints/201126150143-5200.pth"  # 18.249244799876152 DEPTH=2 AGENTS=10
# checkpoint = "./checkpoints/201126160144-2000.pth"  # 18.249244799876152 DEPTH=2 AGENTS=10
checkpoint = "./checkpoints/201127160352-2000.pth"
checkpoint = "./checkpoints/201130083154-2000.pth"

EPSILON = 0.005

# Use last action cache
USE_ACTION_CACHE = False
USE_DEAD_LOCK_AVOIDANCE_AGENT = False  # 21.54485505223213

# Observation parameters (must match training parameters!)
observation_tree_depth = 1
observation_radius = 10
observation_max_path_depth = 30

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

remote_client = FlatlandRemoteClient()

# Observation builder
predictor = ShortestPathPredictorForRailEnv(observation_max_path_depth)
if USE_FAST_TREEOBS:
    def check_is_observation_valid(observation):
        return True


    def get_normalized_observation(observation, tree_depth: int, observation_radius=0):
        return observation


    tree_observation = FastTreeObs(max_depth=observation_tree_depth)
    state_size = tree_observation.observation_dim
else:
    def check_is_observation_valid(observation):
        return observation


    def get_normalized_observation(observation, tree_depth: int, observation_radius=0):
        return normalize_observation(observation, tree_depth, observation_radius)


    tree_observation = TreeObsForRailEnv(max_depth=observation_tree_depth, predictor=predictor)
    # Calculate the state size given the depth of the tree observation and the number of features
    n_features_per_node = tree_observation.observation_dim
    n_nodes = sum([np.power(4, i) for i in range(observation_tree_depth + 1)])
    state_size = n_features_per_node * n_nodes

action_size = 5

# Creates the policy. No GPU on evaluation server.
if not USE_PPO_AGENT:
    policy = DDDQNPolicy(state_size, action_size, Namespace(**{'use_gpu': False}), evaluation_mode=True)
else:
    policy = PPOAgent(state_size, action_size)
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, action_size)

    policy.start_episode(train=False)
    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(train=False)
                if USE_DEAD_LOCK_AVOIDANCE_AGENT:
                    observation = np.zeros((local_env.get_num_agents(), 2))
                for agent_handle in range(nb_agents):

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

                    if info['action_required'][agent_handle]:
                        if agent_handle in agent_last_obs and np.all(
                                agent_last_obs[agent_handle] == observation[agent_handle]):
                            # cache hit
                            action = agent_last_action[agent_handle]
                            nb_hit += 1
                        else:
                            normalized_observation = get_normalized_observation(observation[agent_handle],
                                                                                observation_tree_depth,
                                                                                observation_radius=observation_radius)

                            action = policy.act(normalized_observation, eps=EPSILON)

                    action_dict[agent_handle] = action

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

                policy.end_step(train=False)
                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

    policy.end_episode(train=False)

    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())