import os
import sys
from argparse import Namespace
from pathlib import Path
import numpy as np
import time
import torch
from flatland.core.env_observation_builder import DummyObservationBuilder
from flatland.envs.observations import TreeObsForRailEnv
from flatland.evaluators.client import FlatlandRemoteClient
from flatland.envs.predictions import ShortestPathPredictorForRailEnv
from flatland.evaluators.client import TimeoutException
from utils.deadlock_check import check_if_all_blocked
base_dir = Path(__file__).resolve().parent.parent
sys.path.append(str(base_dir))
from reinforcement_learning.dddqn_policy import DDDQNPolicy
from utils.observation_utils import normalize_observation
####################################################
# EVALUATION PARAMETERS
# Print per-step logs
VERBOSE = True
# Checkpoint to use (remember to push it!)
checkpoint = "checkpoints/201103150429-2500.pth"
# Use last action cache
USE_ACTION_CACHE = True
# 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 = TreeObsForRailEnv(max_depth=observation_tree_depth, predictor=predictor)
# Calculates state and action sizes
n_nodes = sum([np.power(4, i) for i in range(observation_tree_depth + 1)])
state_size = tree_observation.observation_dim * n_nodes
action_size = 5
# Creates the policy. No GPU on evaluation server.
policy = DDDQNPolicy(state_size, action_size, Namespace(**{'use_gpu': False}), evaluation_mode=True)
if os.path.isfile(checkpoint):
policy.load(checkpoint)
else:
print("Checkpoint not found, using untrained policy! (path: {})".format(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
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 = {}
for agent in range(nb_agents):
if observation[agent] and 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:
# otherwise, run normalization and inference
norm_obs = normalize_observation(observation[agent], tree_depth=observation_tree_depth, observation_radius=observation_radius)
action = policy.act(norm_obs, eps=0.0)
action_dict[agent] = action
if USE_ACTION_CACHE:
agent_last_obs[agent] = observation[agent]
agent_last_action[agent] = action
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())