Newer
Older
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/201106234244-400.pth" # 15.64082361736683 Depth 1
checkpoint = "./checkpoints/201106234900-300.pth" # 15.64082361736683 Depth 1
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
# Use last action cache
USE_ACTION_CACHE = False
USE_DEAD_LOCK_AVOIDANCE_AGENT = False
# 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)
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:
#if observation[agent][26] == 1:
# action = RailEnvActions.STOP_MOVING
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
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())