Commit 453cc631 authored by metataro's avatar metataro

Merge branch 'master' into 'sparse-and-deadlock-rewards'

# Conflicts:
#   envs/flatland/utils/gym_env_wrappers.py
#   envs/flatland_sparse.py
parents 8e0ef4b0 d5f7531d
......@@ -127,4 +127,8 @@ dmypy.json
.pyre/
# misc
.idea
\ No newline at end of file
.idea
# custom extras
small_tree_video/
test.yaml
*.json filter=lfs diff=lfs merge=lfs -text
This source diff could not be displayed because it is stored in LFS. You can view the blob instead.
import getopt
import os
import sys
import time
import numpy as np
import pandas as pd
from collections import deque
import gc
from flatland.envs.rail_env import RailEnv
from flatland.utils.misc import str2bool
from flatland.envs.observations import TreeObsForRailEnv
from flatland.envs.predictions import ShortestPathPredictorForRailEnv
from flatland.envs.malfunction_generators import malfunction_from_file
from flatland.envs.rail_generators import rail_from_file
from flatland.envs.schedule_generators import schedule_from_file
from flatland.envs.agent_utils import RailAgentStatus
from utils.observation_utils import normalize_observation # noqa
# from gen_envs import *
import json
from ray.rllib.evaluation.sample_batch_builder import SampleBatchBuilder
from ray.rllib.offline.json_writer import JsonWriter
imitate = True
def main(args):
try:
opts, args = getopt.getopt(args, "", ["sleep-for-animation=", ""])
except getopt.GetoptError as err:
print(str(err)) # will print something like "option -a not recognized"
sys.exit(2)
sleep_for_animation = True
for o, a in opts:
if o in ("--sleep-for-animation"):
sleep_for_animation = str2bool(a)
else:
assert False, "unhandled option"
batch_builder = SampleBatchBuilder() # or MultiAgentSampleBatchBuilder
writer = JsonWriter("./")
# Setting these 2 parameters to True can slow down training
visuals = False
sleep_for_animation = False
if visuals:
from flatland.utils.rendertools import RenderTool
max_depth = 30
tree_depth = 2
trial_start = 100
n_trials = 898
start = 0
columns = ['Agents', 'X_DIM', 'Y_DIM', 'TRIAL_NO',
'REWARD', 'NORMALIZED_REWARD',
'DONE_RATIO', 'STEPS', 'ACTION_PROB']
df_all_results = pd.DataFrame(columns=columns)
for trials in range(trial_start, n_trials + 1):
env_file = f"/Users/flaurent/Sites/flatland/flatland-neurips/envs-100-999/envs/Level_{trials}.pkl"
# env_file = f"../env_configs/test-envs-small/Test_0/Level_{trials}.mpk"
# file = f"../env_configs/actions-small/Test_0/Level_{trials}.mpk"
file = f"/Users/flaurent/Sites/flatland/flatland-neurips/envs-100-999/actions/envs/Level_{trials}.json"
if not os.path.isfile(env_file) or not os.path.isfile(file):
print("Missing file!", env_file, file)
continue
step = 0
obs_builder_object = TreeObsForRailEnv(max_depth=tree_depth,
predictor=ShortestPathPredictorForRailEnv(
max_depth))
env = RailEnv(width=1, height=1,
rail_generator=rail_from_file(env_file),
schedule_generator=schedule_from_file(env_file),
malfunction_generator_and_process_data=malfunction_from_file(
env_file),
obs_builder_object=obs_builder_object)
obs, info = env.reset(
regenerate_rail=True,
regenerate_schedule=True,
activate_agents=False,
random_seed=1001
)
with open(file, "r") as files:
expert_actions = json.load(files)
n_agents = env.get_num_agents()
x_dim, y_dim = env.width, env.height
agent_obs = [None] * n_agents
agent_obs_buffer = [None] * n_agents
done = dict()
done["__all__"] = False
if imitate:
agent_action_buffer = list(
expert_actions[step].values())
else:
# , p=[0.2, 0, 0.5]) # [0] * n_agents
agent_action_buffer = np.random.choice(5, n_agents, replace=True)
update_values = [False] * n_agents
max_steps = int(4 * 2 * (20 + env.height + env.width))
action_size = 5 # 3
# And some variables to keep track of the progress
action_dict = dict()
scores_window = deque(maxlen=100)
reward_window = deque(maxlen=100)
done_window = deque(maxlen=100)
action_prob = [0] * action_size
# agent = Agent(state_size, action_size)
if visuals:
env_renderer = RenderTool(env, gl="PILSVG")
env_renderer.render_env(
show=True, frames=True, show_observations=True)
for a in range(n_agents):
if obs[a]:
agent_obs[a] = normalize_observation(
obs[a], tree_depth, observation_radius=10)
agent_obs_buffer[a] = agent_obs[a].copy()
# Reset score and done
score = 0
agent_action_buffer = np.zeros(n_agents)
# prev_action = np.zeros_like(env.action_space.sample())
prev_reward = np.zeros(n_agents)
for step in range(max_steps):
for a in range(n_agents):
if info['action_required'][a]:
if imitate:
if step < len(expert_actions):
action = expert_actions[step][str(a)]
else:
action = 0
else:
action = 0
action_prob[action] += 1
update_values[a] = True
else:
update_values[a] = False
action = 0
action_dict.update({a: action})
next_obs, all_rewards, done, info = env.step(action_dict)
for a in range(n_agents):
if next_obs[a] is not None:
agent_obs[a] = normalize_observation(
next_obs[a], tree_depth, observation_radius=10)
# Only update the values when we are done or when an action
# was taken and thus relevant information is present
if update_values[a] or done[a]:
start += 1
batch_builder.add_values(
t=step,
eps_id=trials,
agent_index=0,
obs=agent_obs_buffer[a],
actions=action_dict[a],
action_prob=1.0, # put the true action probability
rewards=all_rewards[a],
prev_actions=agent_action_buffer[a],
prev_rewards=prev_reward[a],
dones=done[a],
infos=info['action_required'][a],
new_obs=agent_obs[a])
agent_obs_buffer[a] = agent_obs[a].copy()
agent_action_buffer[a] = action_dict[a]
prev_reward[a] = all_rewards[a]
score += all_rewards[a] # / env.get_num_agents()
if visuals:
env_renderer.render_env(
show=True, frames=True, show_observations=True)
if sleep_for_animation:
time.sleep(0.5)
if done["__all__"] or step > max_steps:
writer.write(batch_builder.build_and_reset())
break
# Collection information about training
if step % 100 == 0:
tasks_finished = 0
for current_agent in env.agents:
if current_agent.status == RailAgentStatus.DONE_REMOVED:
tasks_finished += 1
print(
'\rTrial No {} Training {} Agents on ({},{}).\t Steps {}\t Reward: {:.3f}\t Normalized Reward: {:.3f}\tDones: {:.2f}%\t'.format(
trials, env.get_num_agents(), x_dim, y_dim,
step,
score,
score / (max_steps + n_agents),
100 * np.mean(tasks_finished / max(
1, env.get_num_agents()))), end=" ")
tasks_finished = 0
for current_agent in env.agents:
if current_agent.status == RailAgentStatus.DONE_REMOVED:
tasks_finished += 1
done_window.append(tasks_finished / max(1, env.get_num_agents()))
reward_window.append(score)
scores_window.append(score / (max_steps + n_agents))
data = [[n_agents, x_dim, y_dim,
trials,
np.mean(reward_window),
np.mean(scores_window),
100 * np.mean(done_window),
step, action_prob / np.sum(action_prob)]]
df_cur = pd.DataFrame(data, columns=columns)
df_all_results = pd.concat([df_all_results, df_cur])
if imitate:
df_all_results.to_csv(
f'TreeImitationLearning_DQN_TrainingResults.csv', index=False)
print(
'\rTrial No {} Training {} Agents on ({},{}).\t Total Steps {}\t Reward: {:.3f}\t Normalized Reward: {:.3f}\tDones: {:.2f}%\t'.format(
trials, env.get_num_agents(), x_dim, y_dim,
step,
np.mean(reward_window),
np.mean(scores_window),
100 * np.mean(done_window)))
if visuals:
env_renderer.close_window()
gc.collect()
if __name__ == '__main__':
if 'argv' in globals():
main(sys.argv)
else:
main(sys.argv[1:])
import random
import time
from collections import deque
import numpy as np
from flatland.envs.observations import GlobalObsForRailEnv
from flatland.envs.rail_env import RailEnv
from flatland.envs.rail_generators import complex_rail_generator
from flatland.envs.schedule_generators import complex_schedule_generator
from line_profiler import LineProfiler
from utils.observation_utils import norm_obs_clip, split_tree_into_feature_groups
def printProgressBar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='*'):
"""
Call in a loop to create terminal progress bar
@params:
iteration - Required : current iteration (Int)
total - Required : total iterations (Int)
prefix - Optional : prefix string (Str)
suffix - Optional : suffix string (Str)
decimals - Optional : positive number of decimals in percent complete (Int)
length - Optional : character length of bar (Int)
fill - Optional : bar fill character (Str)
"""
percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total)))
filledLength = int(length * iteration // total)
bar = fill * filledLength + '_' * (length - filledLength)
print('\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end=" ")
# Print New Line on Complete
if iteration == total:
print('')
class RandomAgent:
def __init__(self, state_size, action_size):
self.state_size = state_size
self.action_size = action_size
def act(self, state, eps=0):
"""
:param state: input is the observation of the agent
:return: returns an action
"""
return np.random.choice(np.arange(self.action_size))
def step(self, memories):
"""
Step function to improve agent by adjusting policy given the observations
:param memories: SARS Tuple to be
:return:
"""
return
def save(self, filename):
# Store the current policy
return
def load(self, filename):
# Load a policy
return
def run_test(parameters, agent, test_nr=0, tree_depth=3):
# Parameter initialization
lp = LineProfiler()
features_per_node = 9
start_time_scoring = time.time()
action_dict = dict()
nr_trials_per_test = 5
print('Running Test {} with (x_dim,y_dim) = ({},{}) and {} Agents.'.format(test_nr, parameters[0], parameters[1],
parameters[2]))
# Reset all measurements
time_obs = deque(maxlen=2)
test_scores = []
test_dones = []
# Reset environment
random.seed(parameters[3])
np.random.seed(parameters[3])
nr_paths = max(2, parameters[2] + int(0.5 * parameters[2]))
min_dist = int(min([parameters[0], parameters[1]]) * 0.75)
env = RailEnv(width=parameters[0],
height=parameters[1],
rail_generator=complex_rail_generator(nr_start_goal=nr_paths, nr_extra=5, min_dist=min_dist,
max_dist=99999,
seed=parameters[3]),
schedule_generator=complex_schedule_generator(),
obs_builder_object=GlobalObsForRailEnv(),
number_of_agents=parameters[2])
max_steps = int(3 * (env.height + env.width))
lp_step = lp(env.step)
lp_reset = lp(env.reset)
agent_obs = [None] * env.get_num_agents()
printProgressBar(0, nr_trials_per_test, prefix='Progress:', suffix='Complete', length=20)
for trial in range(nr_trials_per_test):
# Reset the env
lp_reset(True, True)
obs, info = env.reset(True, True)
for a in range(env.get_num_agents()):
data, distance, agent_data = split_tree_into_feature_groups(obs[a], tree_depth)
data = norm_obs_clip(data)
distance = norm_obs_clip(distance)
agent_data = np.clip(agent_data, -1, 1)
obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
for i in range(2):
time_obs.append(obs)
for a in range(env.get_num_agents()):
agent_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
# Run episode
trial_score = 0
for step in range(max_steps):
for a in range(env.get_num_agents()):
action = agent.act(agent_obs[a], eps=0)
action_dict.update({a: action})
# Environment step
next_obs, all_rewards, done, _ = lp_step(action_dict)
for a in range(env.get_num_agents()):
data, distance, agent_data = split_tree_into_feature_groups(next_obs[a], tree_depth)
data = norm_obs_clip(data)
distance = norm_obs_clip(distance)
agent_data = np.clip(agent_data, -1, 1)
next_obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
time_obs.append(next_obs)
for a in range(env.get_num_agents()):
agent_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
trial_score += all_rewards[a] / env.get_num_agents()
if done['__all__']:
break
test_scores.append(trial_score / max_steps)
test_dones.append(done['__all__'])
printProgressBar(trial + 1, nr_trials_per_test, prefix='Progress:', suffix='Complete', length=20)
end_time_scoring = time.time()
tot_test_time = end_time_scoring - start_time_scoring
lp.print_stats()
return test_scores, test_dones, tot_test_time
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