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How to use report_env method in tox
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run.py
Source:run.py 
1import os2import sys3import subprocess4import numpy as np5from time import time, sleep6import argparse7import json8import copy9import tensorflow as tf10import gym11import gym_auv12import gym_auv.reporting13import multiprocessing14from stable_baselines.common import set_global_seeds15from stable_baselines.common.policies import MlpPolicy, MlpLstmPolicy, MlpLnLstmPolicy16from stable_baselines.common.vec_env import VecVideoRecorder, DummyVecEnv, SubprocVecEnv17import stable_baselines.ddpg.policies18import stable_baselines.td3.policies19import stable_baselines.sac.policies20from stable_baselines.ddpg import AdaptiveParamNoiseSpec, NormalActionNoise, LnMlpPolicy, OrnsteinUhlenbeckActionNoise21from stable_baselines import PPO2, DDPG, TD3, A2C, ACER, ACKTR, SAC, TRPO22from sklearn.model_selection import ParameterGrid23from shapely import speedups24from stable_baselines.gail import ExpertDataset25speedups.enable()26DIR_PATH = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))27def _preprocess_custom_envconfig(rawconfig):28 custom_envconfig = dict(zip(args.envconfig[::2], args.envconfig[1::2]))29 for key in custom_envconfig:30 try:31 custom_envconfig[key] = float(custom_envconfig[key])32 if (custom_envconfig[key] == int(custom_envconfig[key])):33 custom_envconfig[key] = int(custom_envconfig[key])34 except ValueError:35 pass36 return custom_envconfig37def create_env(env_id, envconfig, test_mode=False, render_mode='2d', pilot=None, verbose=False):38 if pilot:39 env = gym.make(env_id, env_config=envconfig, test_mode=test_mode, render_mode=render_mode, pilot=pilot, verbose=verbose)40 env.seed(0) # Thomas 04.08.2141 else:42 env = gym.make(env_id, env_config=envconfig, test_mode=test_mode, render_mode=render_mode, verbose=verbose)43 env.seed(0) # Thomas 04.08.2144 return env45def make_mp_env(env_id, rank, envconfig, seed=0, pilot=None):46 """47 Utility function for multiprocessed env.48 :param env_id: (str) the environment ID49 :param num_env: (int) the number of environments you wish to have in subprocesses50 :param seed: (int) the inital seed for RNG51 :param rank: (int) index of the subprocess52 """53 def _init():54 env = create_env(env_id, envconfig, pilot=pilot)55 env.seed(seed + rank)56 return env57 set_global_seeds(seed)58 return _init59def play_scenario(env, recorded_env, args, agent=None):60 # if args.video:61 # print('Recording enabled')62 # recorded_env = VecVideoRecorder(env, args.video_dir, record_video_trigger=lambda x: x == 0, 63 # video_length=args.recording_length, name_prefix=args.video_name64 # )65 from pyglet.window import key66 key_input = np.array([-1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])67 autopilot = False68 # gail_expert_generation = False69 # gail_actions = []70 # gail_observations = []71 # gail_rewards = []72 # gail_num_episodes = 273 # gail_episode_returns = np.zeros((gail_num_episodes,))74 # gail_episode_starts = []75 # gail_reward_sum = 076 # gail_ep_idx = 077 78 print('Playing scenario: ', env)79 def key_press(k, mod):80 nonlocal autopilot81 if k == key.DOWN: key_input[0] = -182 if k == key.UP: key_input[0] = 183 if k == key.LEFT: key_input[1] = 0.584 if k == key.RIGHT: key_input[1] = -0.585 if k == key.NUM_2: key_input[2] = -186 if k == key.NUM_1: key_input[2] = 187 if k == key.J: key_input[3] = -188 if k == key.U: key_input[3] = 189 if k == key.I: key_input[4] = -190 if k == key.K: key_input[4] = 191 if k == key.O: key_input[5] = -192 if k == key.P: key_input[5] = 193 if k == key.NUM_4: key_input[6] = -194 if k == key.NUM_3: key_input[6] = 195 if k == key.A: 96 autopilot = not autopilot97 print('Autopilot {}'.format(autopilot))98 # if k == key.E: 99 # gail_expert_generation = not gail_expert_generation100 # print('gail_expert_generation {}'.format(gail_expert_generation))101 def key_release(k, mod):102 nonlocal restart, quit103 if k == key.R:104 restart = True105 print('Restart')106 if k == key.Q:107 quit = True108 print('quit')109 if k == key.UP: key_input[0] = -1110 if k == key.DOWN: key_input[0] = -1111 if k == key.LEFT and key_input[1] != 0: key_input[1] = 0112 if k == key.RIGHT and key_input[1] != 0: key_input[1] = 0113 if k == key.NUM_2 and key_input[2] != 0: key_input[2] = 0114 if k == key.NUM_1 and key_input[2] != 0: key_input[2] = 0115 if k == key.U and key_input[3] != 0: key_input[3] = 0116 if k == key.J and key_input[3] != 0: key_input[3] = 0117 if k == key.I and key_input[4] != 0: key_input[4] = 0118 if k == key.K and key_input[4] != 0: key_input[4] = 0119 if k == key.O and key_input[5] != 0: key_input[5] = 0120 if k == key.P and key_input[5] != 0: key_input[5] = 0121 if k == key.NUM_4 and key_input[6] != 0: key_input[6] = 0122 if k == key.NUM_3 and key_input[6] != 0: key_input[6] = 0123 viewer = env._viewer2d if args.render in {'both', '2d'} else env._viewer3d124 viewer.window.on_key_press = key_press125 viewer.window.on_key_release = key_release126 try:127 while True:128 t = time()129 restart = False130 t_steps = 0131 quit = False132 if (args.env == 'PathGeneration-v0'):133 a = np.array([5.0, 5.0, 1.0, 1.0])134 elif (args.env == 'PathColavControl-v0'):135 a = np.array([0.0])136 else:137 a = np.array([0.0, 0.0])138 obs = None139 while True:140 t, dt = time(), time()-t141 if args.env == 'PathGeneration-v0':142 a[0] += key_input[1]143 a[1] = max(0, key_input[0], a[1] + 0.1*key_input[0])144 a[2] += 0.1*key_input[2]145 print('Applied action: ', a)146 sleep(1)147 elif (args.env == 'PathColavControl-v0'):148 a[0] = 0.1*key_input[1]149 else:150 a[0] = key_input[0]151 a[1] = key_input[1]152 try:153 env.rewarder.params["lambda"] = np.clip(np.power(10, np.log10(env.rewarder.params["lambda"]) + key_input[2]*0.05), 0, 1)154 env.rewarder.params["eta"] = np.clip(env.rewarder.params["eta"] + key_input[6]*0.02, 0, 4)155 except KeyError:156 pass157 if args.render in {'3d', 'both'}:158 env._viewer3d.camera_height += 0.15*key_input[3]159 env._viewer3d.camera_height = max(0, env._viewer3d.camera_height)160 env._viewer3d.camera_distance += 0.3*key_input[4]161 env._viewer3d.camera_distance = max(1, env._viewer3d.camera_distance)162 env._viewer3d.camera_angle += 0.3*key_input[5]163 elif args.render == '2d':164 env._viewer2d.camera_zoom += 0.1*key_input[4]165 env._viewer2d.camera_zoom = max(0, env._viewer2d.camera_zoom)166 if autopilot and agent is not None:167 if obs is None:168 a = np.array([0.0, 0.0])169 else:170 a, _ = agent.predict(obs, deterministic=True)171 obs, r, done, info = env.step(a)172 173 # gail_observations.append(obs)174 # gail_actions.append(a)175 # gail_rewards.append(r)176 # gail_episode_starts.append(done)177 # gail_reward_sum += r178 # if gail_ep_idx >= gail_num_episodes and gail_expert_generation:179 # break180 if args.verbose > 0:181 print(', '.join('{:.1f}'.format(x) for x in obs) + '(size {})'.format(len(obs)))182 recorded_env.render()183 t_steps += 1184 if args.save_snapshots and not done:185 if t_steps % 50 == 0:186 env.save_latest_episode(save_history=False)187 for size in (100, 200):#, 300, 400, 500):188 gym_auv.reporting.plot_trajectory(189 env, fig_dir='logs/play_results/', fig_prefix=('_t_step_' + str(t_steps) + '_' + str(size)), local=True, size=size190 )191 if quit: raise KeyboardInterrupt192 if done or restart: 193 # gail_episode_returns[gail_ep_idx] = gail_reward_sum194 # gail_reward_sum = 0195 # gail_ep_idx += 1196 break197 198 env.seed(np.random.randint(1000))199 env.save_latest_episode()200 gym_auv.reporting.report(env, report_dir='logs/play_results/')201 gym_auv.reporting.plot_trajectory(env, fig_dir='logs/play_results/')202 env.reset(save_history=False)203 204 # if gail_ep_idx >= gail_num_episodes and gail_expert_generation:205 # gail_observations = np.concatenate(gail_observations).reshape((-1,) + env.observation_space.shape)206 # gail_actions = np.concatenate(gail_actions).reshape((-1,) + env.action_space.shape)207 # gail_rewards = np.array(gail_rewards)208 # gail_episode_starts = np.array(gail_episode_starts[:-1])209 # gail_numpy_dict = {210 # 'actions': gail_actions,211 # 'obs': gail_observations,212 # 'rewards': gail_rewards,213 # 'episode_returns': gail_episode_returns,214 # 'episode_starts': gail_episode_starts215 # }216 # np.savez('gail_expert', **gail_numpy_dict)217 218 219 except KeyboardInterrupt:220 pass221def main(args):222 envconfig_string = args.envconfig223 custom_envconfig = _preprocess_custom_envconfig(args.envconfig) if args.envconfig is not None else {}224 env_id = 'gym_auv:' + args.env225 env_name = env_id.split(':')[-1] if ':' in env_id else env_id226 envconfig = gym_auv.SCENARIOS[env_name]['config'] if env_name in gym_auv.SCENARIOS else {} 227 envconfig.update(custom_envconfig)228 #NUM_CPU = multiprocessing.cpu_count()229 NUM_CPU = 8230 EXPERIMENT_ID = str(int(time())) + args.algo.lower()231 model = {232 'ppo': PPO2,233 'ddpg': DDPG,234 'td3': TD3,235 'a2c': A2C,236 'acer': ACER,237 'acktr': ACKTR,238 'sac': SAC,239 'trpo': TRPO240 }[args.algo.lower()]241 if args.mode == 'play':242 agent = model.load(args.agent) if args.agent is not None else None243 envconfig_play = envconfig.copy()244 envconfig_play['show_indicators'] = True245 #envconfig_play['autocamera3d'] = False246 env = create_env(env_id, envconfig_play, test_mode=True, render_mode=args.render, pilot=args.pilot, verbose=True)247 print('Created environment instance')248 if args.scenario:249 env.load(args.scenario)250 vec_env = DummyVecEnv([lambda: env])251 recorded_env = VecVideoRecorder(vec_env, args.video_dir, record_video_trigger=lambda x: x==0, 252 video_length=args.recording_length, name_prefix=(args.env if args.video_name == 'auto' else args.video_name)253 )254 print(args.video_dir, args.video_name)255 play_scenario(env, recorded_env, args, agent=agent)256 recorded_env.env.close()257 elif (args.mode == 'enjoy'):258 agent = model.load(args.agent)259 figure_folder = os.path.join(DIR_PATH, 'logs', 'enjoys', args.env, EXPERIMENT_ID)260 os.makedirs(figure_folder, exist_ok=True)261 scenario_folder = os.path.join(figure_folder, 'scenarios')262 os.makedirs(scenario_folder, exist_ok=True)263 video_folder = os.path.join(DIR_PATH, 'logs', 'videos', args.env, EXPERIMENT_ID)264 os.makedirs(video_folder, exist_ok=True)265 266 env = create_env(env_id, envconfig, test_mode=True, render_mode=args.render, pilot=args.pilot)267 if args.scenario:268 env.load(args.scenario)269 vec_env = DummyVecEnv([lambda: env])270 recorded_env = VecVideoRecorder(vec_env, video_folder, record_video_trigger=lambda x: x==0, 271 video_length=args.recording_length, name_prefix=(args.env if args.video_name == 'auto' else args.video_name)272 )273 obs = recorded_env.reset()274 state = None275 t_steps = 0276 ep_number = 1277 done = [False for _ in range(vec_env.num_envs)]278 for _ in range(args.recording_length):279 if args.recurrent:280 action, _states = agent.predict(observation=obs, state=state, mask=done, deterministic=not args.stochastic)281 state = _states282 else:283 action, _states = agent.predict(obs, deterministic=not args.stochastic)284 obs, reward, done, info = recorded_env.step(action)285 recorded_env.render()286 t_steps += 1287 288 if t_steps % 800 == 0 or done:289 if not done:290 env.save_latest_episode(save_history=False)291 gym_auv.reporting.plot_trajectory(env, fig_dir=scenario_folder, fig_prefix=(args.env + '_ep{}_step{}'.format(ep_number, t_steps)))292 gym_auv.reporting.plot_trajectory(env, fig_dir=scenario_folder, fig_prefix=(args.env + '_ep{}_step{}_local'.format(ep_number, t_steps)), local=True)293 if done:294 ep_number += 1295 recorded_env.close()296 elif (args.mode == 'train'):297 figure_folder = os.path.join(DIR_PATH, 'logs', 'figures', args.env, EXPERIMENT_ID)298 os.makedirs(figure_folder, exist_ok=True)299 scenario_folder = os.path.join(figure_folder, 'scenarios')300 os.makedirs(scenario_folder, exist_ok=True)301 video_folder = os.path.join(DIR_PATH, 'logs', 'videos', args.env, EXPERIMENT_ID)302 recording_length = 8000303 os.makedirs(video_folder, exist_ok=True)304 agent_folder = os.path.join(DIR_PATH, 'logs', 'agents', args.env, EXPERIMENT_ID)305 os.makedirs(agent_folder, exist_ok=True)306 tensorboard_log = os.path.join(DIR_PATH, 'logs', 'tensorboard', args.env, EXPERIMENT_ID)307 tensorboard_port = 6006308 if args.nomp or model == DDPG or model == TD3 or model == SAC or model == TRPO:309 num_cpu = 1310 vec_env = DummyVecEnv([lambda: create_env(env_id, envconfig, pilot=args.pilot)])311 else:312 num_cpu = NUM_CPU313 vec_env = SubprocVecEnv([make_mp_env(env_id, i, envconfig, pilot=args.pilot) for i in range(num_cpu)]) 314 if (args.agent is not None):315 agent = model.load(args.agent)316 agent.set_env(vec_env)317 else:318 if (model == PPO2):319 if args.recurrent:320 hyperparams = {321 # 'n_steps': 1024,322 # 'nminibatches': 32,323 # 'lam': 0.95,324 # 'gamma': 0.99,325 # 'noptepochs': 10,326 # 'ent_coef': 0.0,327 # 'learning_rate': 0.0003,328 # 'cliprange': 0.2,329 'n_steps': 1024,330 'nminibatches': 1,331 'lam': 0.98,332 'gamma': 0.999,333 'noptepochs': 4,334 'ent_coef': 0.01,335 'learning_rate': 2e-3,336 }337 class CustomLSTMPolicy(MlpLstmPolicy):338 def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm=256, reuse=False, **_kwargs):339 super().__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm, reuse,340 net_arch=[256, 256, 'lstm', dict(vf=[64], pi=[64])],341 **_kwargs)342 agent = PPO2(CustomLSTMPolicy, 343 vec_env, verbose=True, tensorboard_log=tensorboard_log, 344 **hyperparams345 )346 else:347 hyperparams = {348 # 'n_steps': 1024,349 # 'nminibatches': 32,350 # 'lam': 0.95,351 # 'gamma': 0.99,352 # 'noptepochs': 10,353 # 'ent_coef': 0.0,354 # 'learning_rate': 0.0003,355 # 'cliprange': 0.2,356 'n_steps': 1024, # Default 128357 'nminibatches': 32, # Default 4358 'lam': 0.98, # Default 0.95359 'gamma': 0.999, # Default 0.99360 'noptepochs': 4, # Default 4361 'ent_coef': 0.01, # Default 0.01362 'learning_rate': 2e-4, # Default 2.5e-4363 }364 #policy_kwargs = dict(act_fun=tf.nn.tanh, net_arch=[64, 64, 64])365 #policy_kwargs = dict(net_arch=[64, 64, 64])366 #layers = [256, 128, 64]367 layers = [64, 64]368 policy_kwargs = dict(net_arch = [dict(vf=layers, pi=layers)])369 agent = PPO2(MlpPolicy, 370 vec_env, verbose=True, tensorboard_log=tensorboard_log, 371 **hyperparams, policy_kwargs=policy_kwargs372 )373 #dataset = ExpertDataset(expert_path='gail_expert.npz', traj_limitation=1, batch_size=128)374 #print('Pretraining {} agent on "{}"'.format(args.algo.upper(), env_id))375 #agent.pretrain(dataset, n_epochs=1000)376 #print('Done pretraining {} agent on "{}"'.format(args.algo.upper(), env_id))377 elif (model == DDPG):378 # rl-baselines-zoo inspired:379 # hyperparams = {380 # 'memory_limit': 50000,381 # 'normalize_observations': True,382 # 'normalize_returns': False,383 # 'gamma': 0.98,384 # 'actor_lr': 0.00156,385 # 'critic_lr': 0.00156,386 # 'batch_size': 256,387 # 'param_noise': AdaptiveParamNoiseSpec(initial_stddev=0.1, desired_action_stddev=0.1)388 # }389 hyperparams = {390 'memory_limit': 1000000, # Default None (DEPRECATED: use buffer_size instead: 50000)391 'normalize_observations': True, # Default False392 'normalize_returns': False, # Default False393 'gamma': 0.98, # Default 0.99394 'actor_lr': 0.00156, # Default 0.0001395 'critic_lr': 0.00156, # Default 0.001396 'batch_size': 256, # Default 128397 # OpenAI Baselines aim for action_space_stddev = 0.2 for continuous dense cases.398 #'param_noise': AdaptiveParamNoiseSpec(initial_stddev=0.287, desired_action_stddev=0.287)399 # DDPG Paper recommends to add this:400 # OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions))????401 # As action noise to encourage exploration.402 'action_noise': OrnsteinUhlenbeckActionNoise(mean=np.zeros(2), sigma=float(0.5) * np.ones(2))403 }404 agent = DDPG(MlpPolicy,405 vec_env, verbose=True, tensorboard_log=tensorboard_log, **hyperparams406 )407 elif (model == TD3):408 # rl-baselines-zoo inspired:409 # hyperparams = {410 # 'batch_size': 256,411 # 'buffer_size': 50000,412 # 'learning_starts': 1000413 # }414 hyperparams = {415 'buffer_size': 1000000, # Default 50000416 'train_freq': 1000, # Default 100417 'gradient_steps': 1000, # Default 100418 'learning_starts': 10000 # Default 100419 }420 action_noise = NormalActionNoise(mean=np.zeros(2), sigma=0.1*np.ones(2))421 agent = TD3(stable_baselines.td3.MlpPolicy, 422 vec_env, verbose=True, tensorboard_log=tensorboard_log, action_noise=action_noise, **hyperparams423 )424 elif model == A2C:425 # rl-baselines-zoo inspired:426 # hyperparams = {427 # 'n_steps': 5,428 # 'gamma': 0.995,429 # 'ent_coef': 0.00001,430 # 'learning_rate': 0.00083,431 # 'lr_schedule': 'linear'432 # }433 # layers = [256, 128, 64]434 hyperparams = {435 'n_steps': 16, # Default 5436 'gamma': 0.99, # Default 0.99437 'ent_coef': 0.001, # Default 0.01438 'learning_rate': 2e-4, # Default 7e-4439 'lr_schedule': 'linear' # Default 'constant' (learning rate updates)440 }441 layers = [64, 64]442 policy_kwargs = dict(net_arch = [dict(vf=layers, pi=layers)])443 agent = A2C(MlpPolicy, 444 vec_env, verbose=True, tensorboard_log=tensorboard_log, 445 **hyperparams, policy_kwargs=policy_kwargs446 )447 elif model == ACER:448 agent = ACER(MlpPolicy, vec_env, verbose=True, tensorboard_log=tensorboard_log)449 elif model == ACKTR:450 # rl-baselines-zoo inspired:451 # hyperparams = {452 # 'gamma': 0.99,453 # 'n_steps': 16,454 # 'ent_coef': 0.0,455 # 'learning_rate': 0.06,456 # 'lr_schedule': 'constant'457 # }458 # agent = ACKTR(MlpPolicy, vec_env, verbose=True, tensorboard_log=tensorboard_log, **hyperparams)459 agent = ACKTR(MlpPolicy, vec_env, verbose=True, tensorboard_log=tensorboard_log)460 elif model == SAC:461 # rl-baselines-zoo inspired:462 # hyperparams = {463 # 'batch_size': 256,464 # 'learning_starts': 1000465 # }466 # agent = SAC(stable_baselines.sac.MlpPolicy, vec_env, verbose=True, tensorboard_log=tensorboard_log, **hyperparams)467 agent = SAC(stable_baselines.sac.MlpPolicy, vec_env, verbose=True, tensorboard_log=tensorboard_log)468 elif model == TRPO:469 agent = TRPO(MlpPolicy, vec_env, verbose=True, tensorboard_log=tensorboard_log)470 print('Training {} agent on "{}"'.format(args.algo.upper(), env_id))471 n_updates = 0472 n_episodes = 0473 def callback(_locals, _globals):474 nonlocal n_updates475 nonlocal n_episodes476 sys.stdout.write('Training update: {}\r'.format(n_updates))477 sys.stdout.flush()478 _self = _locals['self']479 vec_env = _self.get_env()480 class Struct(object): pass481 report_env = Struct()482 report_env.history = []483 report_env.config = envconfig484 report_env.nsensors = report_env.config["n_sensors_per_sector"]*report_env.config["n_sectors"]485 report_env.sensor_angle = 2*np.pi/(report_env.nsensors + 1)486 report_env.last_episode = vec_env.get_attr('last_episode')[0]487 report_env.config = vec_env.get_attr('config')[0]488 report_env.obstacles = vec_env.get_attr('obstacles')[0]489 env_histories = vec_env.get_attr('history')490 for episode in range(max(map(len, env_histories))):491 for env_idx in range(len(env_histories)):492 if (episode < len(env_histories[env_idx])):493 report_env.history.append(env_histories[env_idx][episode])494 report_env.episode = len(report_env.history) + 1495 total_t_steps = _self.get_env().get_attr('total_t_steps')[0]*num_cpu496 agent_filepath = os.path.join(agent_folder, str(total_t_steps) + '.pkl')497 if model == PPO2:498 recording_criteria = n_updates % 10 == 0499 report_criteria = True500 _self.save(agent_filepath)501 elif model == A2C or model == ACER or model == ACKTR: # or model == TRPO:502 save_criteria = n_updates % 100 == 0503 recording_criteria = n_updates % 1000 == 0504 report_criteria = True505 if save_criteria:506 _self.save(agent_filepath)507 elif model == DDPG or model == TD3 or model == SAC or model == TRPO:508 save_criteria = n_updates % 10000 == 0509 recording_criteria = n_updates % 50000 == 0510 report_criteria = report_env.episode > n_episodes511 if save_criteria:512 _self.save(agent_filepath)513 if report_env.last_episode is not None and len(report_env.history) > 0 and report_criteria:514 try:515 #gym_auv.reporting.plot_trajectory(report_env, fig_dir=scenario_folder, fig_prefix=args.env + '_ep_{}'.format(report_env.episode))516 gym_auv.reporting.report(report_env, report_dir=figure_folder)517 #vec_env.env_method('save', os.path.join(scenario_folder, '_ep_{}'.format(report_env.episode)))518 except OSError as e:519 print("Ignoring reporting OSError:")520 print(repr(e))521 if recording_criteria:522 if args.pilot:523 cmd = 'python run.py enjoy {} --agent "{}" --video-dir "{}" --video-name "{}" --recording-length {} --algo {} --pilot {} --envconfig {}{}'.format(524 args.env, agent_filepath, video_folder, args.env + '-' + str(total_t_steps), recording_length, args.algo, args.pilot, envconfig_string, 525 ' --recurrent' if args.recurrent else ''526 )527 else:528 cmd = 'python run.py enjoy {} --agent "{}" --video-dir "{}" --video-name "{}" --recording-length {} --algo {} --envconfig {}{}'.format(529 args.env, agent_filepath, video_folder, args.env + '-' + str(total_t_steps), recording_length, args.algo, envconfig_string, 530 ' --recurrent' if args.recurrent else ''531 )532 subprocess.Popen(cmd)533 534 n_episodes = report_env.episode535 n_updates += 1536 537 agent.learn(538 total_timesteps=1500000, 539 tb_log_name='log',540 callback=callback541 )542 elif (args.mode in ['policyplot', 'vectorfieldplot', 'streamlinesplot']):543 figure_folder = os.path.join(DIR_PATH, 'logs', 'plots', args.env, EXPERIMENT_ID)544 os.makedirs(figure_folder, exist_ok=True)545 agent = PPO2.load(args.agent)546 if args.testvals:547 testvals = json.load(open(args.testvals, 'r'))548 valuegrid = list(ParameterGrid(testvals))549 for valuedict in valuegrid:550 customconfig = envconfig.copy()551 customconfig.update(valuedict)552 env = create_env(env_id, envconfig, test_mode=True, pilot=args.pilot)553 valuedict_str = '_'.join((key + '-' + str(val) for key, val in valuedict.items()))554 print('Running {} test for {}...'.format(args.mode, valuedict_str))555 556 if args.mode == 'policyplot':557 gym_auv.reporting.plot_actions(env, agent, fig_dir=figure_folder, fig_prefix=valuedict_str)558 elif args.mode == 'vectorfieldplot':559 gym_auv.reporting.plot_vector_field(env, agent, fig_dir=figure_folder, fig_prefix=valuedict_str)560 elif args.mode == 'streamlinesplot':561 gym_auv.reporting.plot_streamlines(env, agent, fig_dir=figure_folder, fig_prefix=valuedict_str)562 else:563 env = create_env(env_id, envconfig, test_mode=True, pilot=args.pilot)564 with open(os.path.join(figure_folder, 'config.json'), 'w') as f:565 json.dump(env.config, f)566 if args.mode == 'policyplot':567 gym_auv.reporting.plot_actions(env, agent, fig_dir=figure_folder)568 elif args.mode == 'vectorfieldplot':569 gym_auv.reporting.plot_vector_field(env, agent, fig_dir=figure_folder)570 elif args.mode == 'streamlinesplot':571 gym_auv.reporting.plot_streamlines(env, agent, fig_dir=figure_folder)572 print('Output folder: ', figure_folder)573 elif args.mode == 'test':574 figure_folder = os.path.join(DIR_PATH, 'logs', 'tests', args.env, EXPERIMENT_ID)575 scenario_folder = os.path.join(figure_folder, 'scenarios')576 video_folder = os.path.join(figure_folder, 'videos')577 os.makedirs(figure_folder, exist_ok=True)578 os.makedirs(scenario_folder, exist_ok=True)579 os.makedirs(video_folder, exist_ok=True)580 if not args.onlyplot:581 agent = model.load(args.agent)582 def create_test_env(video_name_prefix, envconfig=envconfig):583 print('Creating test environment: ' + env_id)584 env = create_env(env_id, envconfig, test_mode=True, render_mode=args.render if args.video else None, pilot=args.pilot)585 vec_env = DummyVecEnv([lambda: env])586 if args.video:587 video_length = min(500, args.recording_length)588 recorded_env = VecVideoRecorder(vec_env, video_folder, record_video_trigger=lambda x: (x%video_length) == 0, 589 video_length=video_length, name_prefix=video_name_prefix590 )591 active_env = recorded_env if args.video else vec_env592 return env, active_env593 failed_tests = []594 def run_test(id, reset=True, report_dir=figure_folder, scenario=None, max_t_steps=None, env=None, active_env=None):595 nonlocal failed_tests596 if env is None or active_env is None:597 env, active_env = create_test_env(video_name_prefix=args.env + '_' + id)598 if scenario is not None:599 obs = active_env.reset()600 env.load(args.scenario)601 print('Loaded', args.scenario)602 else: 603 if reset:604 obs = active_env.reset()605 else:606 obs = env.observe()607 gym_auv.reporting.plot_scenario(env, fig_dir=scenario_folder, fig_postfix=id, show=args.onlyplot)608 if args.onlyplot:609 return610 cumulative_reward = 0611 t_steps = 0612 if max_t_steps is None:613 done = False614 else:615 done = t_steps > max_t_steps616 while not done:617 start_time = time()618 action, _states = agent.predict(obs, deterministic=not args.stochastic)619 obs, reward, done, info = active_env.step(action)620 if args.video:621 active_env.render()622 t_steps += 1623 cumulative_reward += reward[0]624 report_msg = '{:<20}{:<20}{:<20.2f}{:<20.2%}{:0.1f}fps\r'.format(625 id, t_steps, cumulative_reward, info[0]['progress'], 1/(time() - start_time))626 sys.stdout.write(report_msg)627 sys.stdout.flush()628 #if max_t_steps:629 # if t_steps >= max_t_steps:630 # done = True631 if args.save_snapshots and t_steps % 1000 == 0 and not done:632 env.save_latest_episode(save_history=False)633 for size in (20, 50, 100, 200, 300, 400, 500):634 gym_auv.reporting.plot_trajectory(635 env, fig_dir=scenario_folder, fig_prefix=(args.env + '_t_step_' + str(t_steps) + '_' + str(size) + '_' + id), local=True, size=size636 )637 elif done:638 gym_auv.reporting.plot_trajectory(env, fig_dir=scenario_folder, fig_prefix=(args.env + '_' + id))639 env.close()640 gym_auv.reporting.report(env, report_dir=report_dir, lastn=-1)641 #gym_auv.reporting.plot_trajectory(env, fig_dir=scenario_folder, fig_prefix=(args.env + '_' + id))642 #env.save(os.path.join(scenario_folder, id))643 if env.collision:644 failed_tests.append(id)645 with open(os.path.join(figure_folder, 'failures.txt'), 'w') as f:646 f.write(', '.join(map(str, failed_tests)))647 return copy.deepcopy(env.last_episode)648 set_global_seeds(0) # Thomas 04.08.21 --- Ensuring deterministic order of environments for fair intercomparison and plots.649 print('Testing scenario "{}" for {} episodes.\n '.format(args.env, args.episodes))650 report_msg_header = '{:<20}{:<20}{:<20}{:<20}{:<20}{:<20}{:<20}'.format('Episode', 'Timesteps', 'Cum. Reward', 'Progress', 'Collisions', 'CT-Error [m]', 'H-Error [deg]')651 print(report_msg_header)652 print('-'*len(report_msg_header)) 653 if args.testvals:654 testvals = json.load(open(args.testvals, 'r'))655 valuegrid = list(ParameterGrid(testvals))656 if args.scenario:657 if args.testvals:658 episode_dict = {}659 for valuedict in valuegrid:660 customconfig = envconfig.copy()661 customconfig.update(valuedict)662 env, active_env = create_test_env(envconfig=customconfig)663 valuedict_str = '_'.join((key + '-' + str(val) for key, val in valuedict.items()))664 colorval = -np.log10(valuedict['reward_lambda']) #should be general665 666 rep_subfolder = os.path.join(figure_folder, valuedict_str)667 os.makedirs(rep_subfolder, exist_ok=True)668 for episode in range(args.episodes):669 last_episode = run_test(valuedict_str + '_ep' + str(episode), report_dir=rep_subfolder)670 episode_dict[valuedict_str] = [last_episode, colorval]671 print('Plotting all')672 gym_auv.reporting.plot_trajectory(env, fig_dir=scenario_folder, fig_prefix=(args.env + '_all_agents'), episode_dict=episode_dict)673 else:674 run_test("ep0", reset=True, scenario=args.scenario, max_t_steps=5000)675 else:676 if args.testvals:677 episode_dict = {}678 agent_index = 1679 for valuedict in valuegrid:680 customconfig = envconfig.copy()681 customconfig.update(valuedict)682 env, active_env = create_test_env(envconfig=customconfig)683 valuedict_str = '_'.join((key + '-' + str(val) for key, val in valuedict.items()))684 colorval = np.log10(valuedict['reward_lambda']) #should be general685 686 rep_subfolder = os.path.join(figure_folder, valuedict_str)687 os.makedirs(rep_subfolder, exist_ok=True)688 for episode in range(args.episodes):689 last_episode = run_test(valuedict_str + '_ep' + str(episode), report_dir=rep_subfolder)690 episode_dict['Agent ' + str(agent_index)] = [last_episode, colorval]691 agent_index += 1692 693 gym_auv.reporting.plot_trajectory(env, fig_dir=figure_folder, fig_prefix=(args.env + '_all_agents'), episode_dict=episode_dict)694 else:695 env, active_env = create_test_env(video_name_prefix=args.env)696 for episode in range(args.episodes):697 run_test('ep' + str(episode), env=env, active_env=active_env, max_t_steps=10000)698 if args.video and active_env:699 active_env.close()700if __name__ == '__main__':701 print("WARNING: DETERMINISTIC SEED ACTIVATED")702 parser = argparse.ArgumentParser()703 parser.add_argument(704 'mode',705 help='Which program mode to run.',706 choices=['play', 'train', 'enjoy', 'test', 'policyplot', 'vectorfieldplot', 'streamlinesplot'],707 )708 parser.add_argument(709 'env',710 help='Name of the gym environment to run.',711 choices=gym_auv.SCENARIOS.keys()712 )713 parser.add_argument(714 '--agent',715 help='Path to the RL agent to simulate.',716 )717 parser.add_argument(718 '--video-dir',719 help='Dir for output video.',720 default='logs/videos/'721 )722 parser.add_argument(723 '--video-name',724 help='Name of output video.',725 default='auto'726 )727 parser.add_argument(728 '--algo',729 help='RL algorithm to use.',730 default='ppo',731 choices=['ppo', 'ddpg', 'td3', 'a2c', 'acer', 'acktr', 'sac','trpo']732 )733 parser.add_argument(734 '--render',735 help='Rendering mode to use.',736 default='2d',737 choices=['2d', '3d', 'both'] #'both' currently broken738 )739 parser.add_argument(740 '--recording-length',741 help='Timesteps to simulate in enjoy mode.',742 type=int,743 default=2000744 )745 parser.add_argument(746 '--episodes',747 help='Number of episodes to simulate in test mode.',748 type=int,749 default=1750 )751 parser.add_argument(752 '--video',753 help='Record video for test mode.',754 action='store_true'755 )756 parser.add_argument(757 '--onlyplot',758 help='Skip simulations, only plot scenario.',759 action='store_true'760 )761 parser.add_argument(762 '--scenario',763 help='Path to scenario file containing environment data to be loaded.',764 )765 parser.add_argument(766 '--verbose',767 help='Print debugging information.',768 action='store_true'769 )770 parser.add_argument(771 '--envconfig',772 help='Override environment config parameters.',773 nargs='*'774 )775 parser.add_argument(776 '--nomp',777 help='Only use single CPU core for training.',778 action='store_true'779 )780 parser.add_argument(781 '--stochastic',782 help='Use stochastic actions.',783 action='store_true'784 )785 parser.add_argument(786 '--recurrent',787 help='Use RNN for policy network.',788 action='store_true'789 )790 parser.add_argument(791 '--pilot',792 help='If training in a controller environment, this is the pilot agent to control.',793 )794 parser.add_argument(795 '--testvals',796 help='Path to JSON file containing config values to test.',797 )798 parser.add_argument(799 '--save-snapshots',800 help='Save snapshots of the vessel trajectory on a fixed interval.',801 )802 args = parser.parse_args()803 from win10toast import ToastNotifier804 toaster = ToastNotifier()805 try:806 main(args)807 toaster.show_toast("run.py", "Program is done", duration=10)808 except Exception as e:809 toaster.show_toast("run.py", "Program has crashed", duration=10)810 raise e...
train.py
Source:train.py
1import gym2import gym_turbine3import os4from time import time5import multiprocessing6import argparse7import numpy as np8import json9from gym_turbine import reporting10from stable_baselines3 import PPO11from stable_baselines3.common.env_util import make_vec_env12from stable_baselines3.common.vec_env import SubprocVecEnv13from stable_baselines3.common.callbacks import BaseCallback, CallbackList, CheckpointCallback14hyperparams = {15 'n_steps': 1024,16 'nminibatches': 256,17 'learning_rate': 1e-5,18 'lam': 0.95,19 'gamma': 0.99,20 'noptepochs': 4,21 'cliprange': 0.2,22 'ent_coef': 0.01,23}24class ReportingCallback(BaseCallback):25 """26 Callback for reporting training27 :param report_dir: Path to the folder where the report will be saved.28 :param verbose:29 """30 def __init__(self, report_dir: str, verbose: int = 0):31 super(ReportingCallback, self).__init__(verbose)32 self.report_dir = report_dir33 self.verbose = verbose34 def _on_step(self) -> bool:35 # check if env is done, if yes report it to csv file36 done_array = np.array(self.locals.get("done") if self.locals.get("done") is not None else self.locals.get("dones"))37 if np.sum(done_array).item() > 0:38 env_histories = self.training_env.get_attr('history')39 class Struct(object): pass40 report_env = Struct()41 report_env.history = []42 for env_idx in range(len(done_array)):43 if done_array[env_idx]:44 report_env.history.append(env_histories[env_idx])45 reporting.report(env=report_env, report_dir=self.report_dir)46 if self.verbose:47 print("reported episode to file")48 return True49 def _on_training_end(self) -> None:50 """51 This event is triggered before exiting the `learn()` method.52 """53 vec_env = self.training_env54 env_histories = vec_env.get_attr('total_history')55 class Struct(object): pass56 report_env = Struct()57 report_env.history = []58 for episode in range(max(map(len, env_histories))):59 for env_idx in range(len(env_histories)):60 if (episode < len(env_histories[env_idx])):61 report_env.history.append(env_histories[env_idx][episode])62 if len(report_env.history) > 0:63 training_data = reporting.format_history(report_env, lastn=-1)64 reporting.make_summary_file(training_data, self.report_dir)65 if self.verbose:66 print("Made summary file of training")67class TensorboardCallback(BaseCallback):68 """69 Custom callback for plotting additional values in tensorboard.70 """71 def __init__(self, verbose=0):72 super(TensorboardCallback, self).__init__(verbose)73 def _on_step(self) -> bool:74 done_array = np.array(self.locals.get("done") if self.locals.get("done") is not None else self.locals.get("dones"))75 if np.sum(done_array).item():76 history = self.training_env.get_attr('history')77 for env_idx in range(len(done_array)):78 if done_array[env_idx]:79 self.logger.record_mean('custom/reward', history[env_idx]['reward'])80 self.logger.record_mean('custom/crashed', history[env_idx]['crashed'])81 return True82if __name__ == '__main__':83 NUM_CPUs = multiprocessing.cpu_count()84 parser = argparse.ArgumentParser()85 parser.add_argument(86 '--timesteps',87 type=int,88 default=500000,89 help='Number of timesteps to train the agent. Default=500000',90 )91 parser.add_argument(92 '--agent',93 help='Path to the RL agent to continue training from.',94 )95 parser.add_argument(96 '--note',97 type=str,98 default=None,99 help="Note with additional info about training"100 )101 parser.add_argument(102 '--no_reporting',103 help='Skip reporting to increase framerate',104 action='store_true'105 )106 args = parser.parse_args()107 # Define necessary directories108 EXPERIMENT_ID = str(int(time())) + 'ppo'109 agents_dir = os.path.join('logs', EXPERIMENT_ID, 'agents')110 os.makedirs(agents_dir, exist_ok=True)111 report_dir = os.path.join('logs', EXPERIMENT_ID, 'training_report')112 tensorboard_log = os.path.join('logs', EXPERIMENT_ID, 'tensorboard')113 # Make environment (NUM_CPUs parallel envs)114 env = make_vec_env('TurbineStab-v0', n_envs=NUM_CPUs, vec_env_cls=SubprocVecEnv)115 # Write note and config to Note.txt file116 with open(os.path.join('logs', EXPERIMENT_ID, "Note.txt"), "a") as file_object:117 file_object.write("env_config: " + json.dumps(env.get_attr('config')[0]))118 if args.note:119 file_object.write(args.note)120 # Callback to save model at checkpoints during training121 checkpoint_callback = CheckpointCallback(save_freq=1000, save_path=agents_dir)122 # Callback to report training to file123 reporting_callback = ReportingCallback(report_dir=report_dir, verbose=True)124 # Callback to report additional values to tensorboard125 tensorboard_callback = TensorboardCallback(verbose=True)126 # Create the callback list127 if args.no_reporting:128 callback = CallbackList([checkpoint_callback, tensorboard_callback])129 else:130 callback = CallbackList([checkpoint_callback, reporting_callback, tensorboard_callback])131 if (args.agent is not None):132 agent = PPO.load(args.agent, env=env, verbose=True, tensorboard_log=tensorboard_log)133 else:134 agent = PPO('MlpPolicy', env, verbose=True, tensorboard_log=tensorboard_log)135 agent.learn(total_timesteps=args.timesteps, callback=callback)136 # Save trained agent137 agent_path = os.path.join(agents_dir, "last_model_" + str(args.timesteps))138 agent.save(agent_path)...
show_env.py
Source:show_env.py
...7 extra = [e for e in env_conf if e not in ignore] if all_envs else []8 if description and default:9 report.line("default environments:")10 max_length = max(len(env) for env in (default + extra) or [""])11 def report_env(e):12 if description:13 text = env_conf[e].description or "[no description]"14 msg = "{} -> {}".format(e.ljust(max_length), text).strip()15 else:16 msg = e17 report.line(msg)18 for e in default:19 report_env(e)20 if all_envs and extra:21 if description:22 if default:23 report.line("")24 report.line("additional environments:")25 for e in extra:...
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