'''
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())