def enjoy(stats_path,
model_path,
dataset,
body_id,
algo,
n_timesteps=200,
test_time=3,
render=False,
seed=0):
dataset_name, env_id, train_files, train_params, train_names, test_files, test_params, test_names = load_dataset.load_dataset(
dataset, seed=0, shuffle=False, train_proportion=1)
set_random_seed(seed * 128 + 127)
hyperparams, stats_path = get_saved_hyperparams(stats_path,
norm_reward=False,
test_mode=True)
env_kwargs = {
"xml": train_files[body_id],
"param": train_params[body_id],
"max_episode_steps": n_timesteps + 1,
"render": render,
}
env = create_test_env(
env_id,
n_envs=1,
stats_path=stats_path,
seed=seed,
log_dir="tmp/",
should_render=False,
hyperparams=hyperparams,
env_kwargs=env_kwargs,
)
kwargs = dict(seed=seed)
model = ALGOS[algo].load(model_path, env=env, **kwargs)
obs = env.reset()
state = None
episode_reward = 0.0
episode_rewards, episode_lengths = [], []
ep_len = 0
body_x_record = []
for _run in range(test_time):
body_x = 0
for _step in range(n_timesteps):
action, state = model.predict(obs, state=state, deterministic=True)
if isinstance(env.action_space, gym.spaces.Box):
action = np.clip(action, env.action_space.low,
env.action_space.high)
body_x = env.envs[0].robot.body_xyz[0]
obs, reward, done, infos = env.step(action)
episode_reward += reward[0]
ep_len += 1
if render:
sleep(0.01)
if done:
break
body_x_record.append(body_x)
obs = env.reset()
body_x_record = np.array(body_x_record)
env.close()
return body_x_record
def get_scores(args, folder, policy_file, env_name, algo, stats_path, hyperparams, n_evals):
"""
"""
env = create_test_env(
env_name,
n_envs=10,
stats_path=stats_path,
seed=args.seed,
log_dir=folder + "/../Logs",
should_render=not args.no_render,
hyperparams=hyperparams,
env_kwargs={},
)
fields = policy_file.split('.')
#custom_objects = {}
# This part is useful if the generated files come from a more recent version of python
# newer_python_version = sys.version_info.major == 3 and sys.version_info.minor >= 8
# if newer_python_version:
custom_objects = {
"learning_rate": 0.0,
"lr_schedule": lambda _: 0.0,
"exploration_schedule": lambda _: 0.0,
"clip_range": lambda _: 0.0,
}
Space.__setstate__ = patch_setstate
model = ALGOS[algo].load(folder + "/" + fields[0], custom_objects=custom_objects)
policy = model.policy
episode_rewards, _ = evaluate_policy(policy, env, n_eval_episodes=n_evals, return_episode_rewards=True)
scores = np.array(episode_rewards)
return scores
def __init__(self,
env_id,
video_folder,
hyperparams,
params_path,
video_length,
deterministic=True,
name_prefix='video',
interval=10000,
env_params={},
seed=0):
"""
:param env_id: environment id
:param video_folder: (str) Where to save videos
:param video_length: (int) Length of recorded videos
:param name_prefix: (str) Prefix to the video name
:param interval:
"""
#ToDo: finish the doc string
self.best_mean_reward = -np.inf
self.n_steps = 0
self.video_folder = video_folder
self.interval = interval
self.video_length = video_length
self.deterministic = deterministic
self.is_atari = 'NoFrameskip' in env_id
self.env_id = env_id
test_path = os.path.join(video_folder, 'video')
env = create_test_env(env_id,
n_envs=1,
stats_path=params_path,
seed=seed,
hyperparams=hyperparams,
env_params=env_params)
env.reset()
self.env = VecVideoRecorder(env,
test_path,
record_video_trigger=lambda x: x == 0,
video_length=video_length,
name_prefix=name_prefix)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env',
help='environment ID',
type=str,
default='CartPole-v1')
parser.add_argument('-f',
'--folder',
help='Log folder',
type=str,
default='trained_agents')
parser.add_argument('--algo',
help='RL Algorithm',
default='ppo2',
type=str,
required=False,
choices=list(ALGOS.keys()))
parser.add_argument('-n',
'--n-timesteps',
help='number of timesteps',
default=1000,
type=int)
parser.add_argument('--n-envs',
help='number of environments',
default=1,
type=int)
parser.add_argument(
'--exp-id',
help='Experiment ID (default: -1, no exp folder, 0: latest)',
default=-1,
type=int)
parser.add_argument('--verbose',
help='Verbose mode (0: no output, 1: INFO)',
default=1,
type=int)
parser.add_argument(
'--no-render',
action='store_true',
default=False,
help='Do not render the environment (useful for tests)')
parser.add_argument('--deterministic',
action='store_true',
default=False,
help='Use deterministic actions')
parser.add_argument('--stochastic',
action='store_true',
default=False,
help='Use stochastic actions (for DDPG/DQN/SAC)')
parser.add_argument(
'--norm-reward',
action='store_true',
default=False,
help='Normalize reward if applicable (trained with VecNormalize)')
parser.add_argument('--seed',
help='Random generator seed',
type=int,
default=0)
parser.add_argument('--reward-log',
help='Where to log reward',
default='',
type=str)
parser.add_argument(
'--gym-packages',
type=str,
nargs='+',
default=[],
help=
'Additional external Gym environemnt package modules to import (e.g. gym_minigrid)'
)
args = parser.parse_args()
# Going through custom gym packages to let them register in the global registory
for env_module in args.gym_packages:
importlib.import_module(env_module)
env_id = args.env
algo = args.algo
folder = args.folder
if args.exp_id == 0:
args.exp_id = get_latest_run_id(os.path.join(folder, algo), env_id)
print('Loading latest experiment, id={}'.format(args.exp_id))
# Sanity checks
if args.exp_id > 0:
log_path = os.path.join(folder, algo,
'{}_{}'.format(env_id, args.exp_id))
else:
log_path = os.path.join(folder, algo)
assert os.path.isdir(log_path), "The {} folder was not found".format(
log_path)
model_path = find_saved_model(algo, log_path, env_id)
if algo in ['dqn', 'ddpg', 'sac', 'td3']:
args.n_envs = 1
set_global_seeds(args.seed)
is_atari = 'NoFrameskip' in env_id
stats_path = os.path.join(log_path, env_id)
hyperparams, stats_path = get_saved_hyperparams(
stats_path, norm_reward=args.norm_reward, test_mode=True)
log_dir = args.reward_log if args.reward_log != '' else None
env = create_test_env(env_id,
n_envs=args.n_envs,
is_atari=is_atari,
stats_path=stats_path,
seed=args.seed,
log_dir=log_dir,
should_render=not args.no_render,
hyperparams=hyperparams)
# ACER raises errors because the environment passed must have
# the same number of environments as the model was trained on.
load_env = None if algo == 'acer' else env
model = ALGOS[algo].load(model_path, env=load_env)
obs = env.reset()
# Force deterministic for DQN, DDPG, SAC and HER (that is a wrapper around)
deterministic = args.deterministic or algo in [
'dqn', 'ddpg', 'sac', 'her', 'td3'
] and not args.stochastic
episode_reward = 0.0
episode_rewards = []
ep_len = 0
# For HER, monitor success rate
successes = []
for _ in range(args.n_timesteps):
action, _ = model.predict(obs, deterministic=deterministic)
# Random Agent
# action = [env.action_space.sample()]
# Clip Action to avoid out of bound errors
if isinstance(env.action_space, gym.spaces.Box):
action = np.clip(action, env.action_space.low,
env.action_space.high)
obs, reward, done, infos = env.step(action)
if not args.no_render:
env.render('human')
episode_reward += reward[0]
ep_len += 1
if args.n_envs == 1:
# For atari the return reward is not the atari score
# so we have to get it from the infos dict
if is_atari and infos is not None and args.verbose >= 1:
episode_infos = infos[0].get('episode')
if episode_infos is not None:
print("Atari Episode Score: {:.2f}".format(
episode_infos['r']))
print("Atari Episode Length", episode_infos['l'])
if done and not is_atari and args.verbose > 0:
# NOTE: for env using VecNormalize, the mean reward
# is a normalized reward when `--norm_reward` flag is passed
print("Episode Reward: {:.2f}".format(episode_reward))
print("Episode Length", ep_len)
episode_rewards.append(episode_reward)
episode_reward = 0.0
ep_len = 0
# Reset also when the goal is achieved when using HER
if done or infos[0].get('is_success', False):
if args.algo == 'her' and args.verbose > 1:
print("Success?", infos[0].get('is_success', False))
# Alternatively, you can add a check to wait for the end of the episode
# if done:
obs = env.reset()
if args.algo == 'her':
successes.append(infos[0].get('is_success', False))
episode_reward, ep_len = 0.0, 0
if args.verbose > 0 and len(successes) > 0:
print("Success rate: {:.2f}%".format(100 * np.mean(successes)))
if args.verbose > 0 and len(episode_rewards) > 0:
print("Mean reward: {:.2f}".format(np.mean(episode_rewards)))
# Workaround for https://github.com/openai/gym/issues/893
if not args.no_render:
if args.n_envs == 1 and 'Bullet' not in env_id and not is_atari and isinstance(
env, VecEnv):
# DummyVecEnv
# Unwrap env
while isinstance(env, VecNormalize) or isinstance(
env, VecFrameStack):
env = env.venv
env.envs[0].env.close()
else:
# SubprocVecEnv
env.close()
def main():
seed = 0
num_samples = 20
parser = argparse.ArgumentParser()
parser.add_argument('--env',
help='environment ID',
type=str,
default='CartPole-v1')
parser.add_argument('-f',
'--folder',
help='Log folder',
type=str,
default='rl-baselines-zoo/trained_agents')
parser.add_argument('--algo',
help='RL Algorithm',
default='dqn',
type=str,
required=False,
choices=list(ALGOS.keys()))
parser.add_argument('-n',
'--n-timesteps',
help='number of timesteps',
default=2000,
type=int)
parser.add_argument('--n-envs',
help='number of environments',
default=1,
type=int)
parser.add_argument(
'--exp-id',
help='Experiment ID (default: -1, no exp folder, 0: latest)',
default=-1,
type=int)
parser.add_argument('--verbose',
help='Verbose mode (0: no output, 1: INFO)',
default=1,
type=int)
parser.add_argument(
'--no-render',
action='store_true',
default=False,
help='Do not render the environment (useful for tests)')
parser.add_argument('--deterministic',
action='store_true',
default=False,
help='Use deterministic actions')
parser.add_argument('--stochastic',
action='store_true',
default=False,
help='Use stochastic actions (for DDPG/DQN/SAC)')
parser.add_argument(
'--load-best',
action='store_true',
default=False,
help='Load best model instead of last model if available')
parser.add_argument(
'--norm-reward',
action='store_true',
default=False,
help='Normalize reward if applicable (trained with VecNormalize)')
args = parser.parse_args()
env_id = args.env
algo = args.algo
folder = args.folder
if args.exp_id == 0:
args.exp_id = get_latest_run_id(os.path.join(folder, algo), env_id)
print('Loading latest experiment, id={}'.format(args.exp_id))
# Sanity checks
if args.exp_id > 0:
log_path = os.path.join(folder, algo,
'{}_{}'.format(env_id, args.exp_id))
else:
log_path = os.path.join(folder, algo)
assert os.path.isdir(log_path), "The {} folder was not found".format(
log_path)
model_path = find_saved_model(algo,
log_path,
env_id,
load_best=args.load_best)
if algo in ['dqn', 'ddpg', 'sac', 'td3']:
args.n_envs = 1
set_global_seeds(seed)
is_atari = 'NoFrameskip' in env_id
stats_path = os.path.join(log_path, env_id)
hyperparams, stats_path = get_saved_hyperparams(
stats_path, norm_reward=args.norm_reward, test_mode=True)
log_dir = None
env_kwargs = {}
env = create_test_env(env_id,
n_envs=args.n_envs,
is_atari=is_atari,
stats_path=stats_path,
seed=seed,
log_dir=log_dir,
should_render=not args.no_render,
hyperparams=hyperparams,
env_kwargs=env_kwargs)
# ACER raises errors because the environment passed must have
# the same number of environments as the model was trained on.
load_env = None if algo == 'acer' else env
model = ALGOS[algo].load(model_path, env=load_env)
env = gym.make('CartPole-v1')
obs = env.reset()
# Force deterministic for DQN, DDPG, SAC and HER (that is a wrapper around)
deterministic = args.deterministic or algo in [
'dqn', 'ddpg', 'sac', 'her', 'td3'
] and not args.stochastic
episode_reward = 0.0
episode_rewards, episode_lengths = [], []
ep_len = 0
# For HER, monitor success rate
successes = []
state = None
embedder = indicator_feature
halfspaces = {}
for i in range(num_samples):
print("+" * 10)
#sample random state to start in
#TODO: maybe reset with random actions? How to make it realistic? Does it matter. Let's just try random for now to test weird edge cases.
obs = env.reset(uniform=True) #sample more uniformly than typical
print("start state", obs)
# input()
#obs = env.reset_state(env.observation_space.sample())
#rollout once for each action and compute feature counts
start_state = obs.copy()
fcount_vectors = []
init_actions = []
##rollout code:
for init_action in range(env.action_space.n):
print("ACTION", init_action)
obs = env.reset(start_state=start_state)
print("init state", obs)
env.render()
# input()
ep_ret = 0
fcounts = embedder(start_state)
#do initial action
obs, r, done, info = env.step(init_action) # take a random action
fcounts += embedder(obs) #TODO: discount??
ep_ret += r
#print(r, obs)
if done:
print("final state", obs)
print("return", ep_ret)
print("fcounts", fcounts)
fcount_vectors.append(fcounts)
init_actions.append(init_action)
continue
#run tester policy thereafter
while True:
#env.render()
#TODO: sample within allowable range of angle and position
action, state = model.predict(obs,
state=state,
deterministic=deterministic)
# Random Agent
# action = [env.action_space.sample()]
# Clip Action to avoid out of bound errors
if isinstance(env.action_space, gym.spaces.Box):
action = np.clip(action, env.action_space.low,
env.action_space.high)
#a = env.action_space.sample()
#print(obs, action)
obs, r, done, info = env.step(action) # take a random action
fcounts += embedder(obs)
#print(obs)
#print(done)
ep_ret += r
#print(r, obs)
if done:
print("final state", obs)
print("return", ep_ret)
print("fcounts", fcounts)
fcount_vectors.append(fcounts)
init_actions.append(init_action)
break
print("action {} over {} => fcount diff = {}".format(
init_actions[0], init_actions[1],
fcount_vectors[0] - fcount_vectors[1]))
halfspaces[state, init_actions[0],
init_actions[1]] = fcount_vectors[0] - fcount_vectors[1]
# input()
#TODO: put this inside one of the value alignment verification classes to get sa_fcount_diffs and hopefully reuse that code
#then visualize test cases
# input()
# for _ in range(args.n_timesteps):
# action, state = model.predict(obs, state=state, deterministic=deterministic)
# # Random Agent
# # action = [env.action_space.sample()]
# # Clip Action to avoid out of bound errors
# if isinstance(env.action_space, gym.spaces.Box):
# action = np.clip(action, env.action_space.low, env.action_space.high)
# obs, reward, done, infos = env.step(action)
# if not args.no_render:
# env.render('human')
# episode_reward += reward
# ep_len += 1
# if args.n_envs == 1:
# # For atari the return reward is not the atari score
# # so we have to get it from the infos dict
# if is_atari and infos is not None and args.verbose >= 1:
# episode_infos = infos.get('episode')
# if episode_infos is not None:
# print("Atari Episode Score: {:.2f}".format(episode_infos['r']))
# print("Atari Episode Length", episode_infos['l'])
# if done and not is_atari and args.verbose > 0:
# # NOTE: for env using VecNormalize, the mean reward
# # is a normalized reward when `--norm_reward` flag is passed
# print("Episode Reward: {:.2f}".format(episode_reward))
# print("Episode Length", ep_len)
# state = None
# episode_rewards.append(episode_reward)
# episode_lengths.append(ep_len)
# episode_reward = 0.0
# ep_len = 0
# # Reset also when the goal is achieved when using HER
# if done or infos.get('is_success', False):
# if args.algo == 'her' and args.verbose > 1:
# print("Success?", infos[0].get('is_success', False))
# # Alternatively, you can add a check to wait for the end of the episode
# # if done:
# obs = env.reset()
# if args.algo == 'her':
# successes.append(infos[0].get('is_success', False))
# episode_reward, ep_len = 0.0, 0
# if args.verbose > 0 and len(successes) > 0:
# print("Success rate: {:.2f}%".format(100 * np.mean(successes)))
# if args.verbose > 0 and len(episode_rewards) > 0:
# print("Mean reward: {:.2f} +/- {:.2f}".format(np.mean(episode_rewards), np.std(episode_rewards)))
# if args.verbose > 0 and len(episode_lengths) > 0:
# print("Mean episode length: {:.2f} +/- {:.2f}".format(np.mean(episode_lengths), np.std(episode_lengths)))
# Workaround for https://github.com/openai/gym/issues/893
if not args.no_render:
if args.n_envs == 1 and 'Bullet' not in env_id and not is_atari and isinstance(
env, VecEnv):
# DummyVecEnv
# Unwrap env
while isinstance(env, VecNormalize) or isinstance(
env, VecFrameStack):
env = env.venv
env.envs[0].env.close()
else:
# SubprocVecEnv
env.close()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env',
help='environment ID',
type=str,
default='CartPole-v1')
parser.add_argument('-f',
'--folder',
help='Log folder',
type=str,
default='trained_agents')
parser.add_argument('--algo',
help='RL Algorithm',
default='ppo2',
type=str,
required=False,
choices=list(ALGOS.keys()))
parser.add_argument('-n',
'--n-timesteps',
help='number of timesteps',
default=1000,
type=int)
parser.add_argument('--n-envs',
help='number of environments',
default=1,
type=int)
parser.add_argument('--verbose',
help='Verbose mode (0: no output, 1: INFO)',
default=1,
type=int)
parser.add_argument(
'--no-render',
action='store_true',
default=False,
help='Do not render the environment (useful for tests)')
parser.add_argument('--deterministic',
action='store_true',
default=False,
help='Use deterministic actions')
parser.add_argument(
'--norm-reward',
action='store_true',
default=False,
help='Normalize reward if applicable (trained with VecNormalize)')
parser.add_argument('--seed',
help='Random generator seed',
type=int,
default=0)
parser.add_argument('--reward-log',
help='Where to log reward',
default='',
type=str)
args = parser.parse_args()
env_id = args.env
algo = args.algo
folder = args.folder
model_path = "{}/{}/{}.pkl".format(folder, algo, env_id)
# Sanity checks
assert os.path.isdir(folder + '/' +
algo), "The {}/{}/ folder was not found".format(
folder, algo)
assert os.path.isfile(
model_path), "No model found for {} on {}, path: {}".format(
algo, env_id, model_path)
if algo in ['dqn', 'ddpg']:
args.n_envs = 1
set_global_seeds(args.seed)
is_atari = 'NoFrameskip' in env_id
stats_path = "{}/{}/{}/".format(folder, algo, env_id)
if not os.path.isdir(stats_path):
stats_path = None
log_dir = args.reward_log if args.reward_log != '' else None
env = create_test_env(env_id,
n_envs=args.n_envs,
is_atari=is_atari,
stats_path=stats_path,
norm_reward=args.norm_reward,
seed=args.seed,
log_dir=log_dir,
should_render=not args.no_render)
model = ALGOS[algo].load(model_path)
obs = env.reset()
# Force deterministic for DQN and DDPG
deterministic = args.deterministic or algo in ['dqn', 'ddpg']
running_reward = 0.0
ep_len = 0
for _ in range(args.n_timesteps):
action, _ = model.predict(obs, deterministic=deterministic)
# Random Agent
# action = [env.action_space.sample()]
# Clip Action to avoid out of bound errors
if isinstance(env.action_space, gym.spaces.Box):
action = np.clip(action, env.action_space.low,
env.action_space.high)
obs, reward, done, infos = env.step(action)
if not args.no_render:
env.render('human')
running_reward += reward[0]
ep_len += 1
if args.n_envs == 1:
# For atari the return reward is not the atari score
# so we have to get it from the infos dict
if is_atari and infos is not None and args.verbose >= 1:
episode_infos = infos[0].get('episode')
if episode_infos is not None:
print("Atari Episode Score: {:.2f}".format(
episode_infos['r']))
print("Atari Episode Length", episode_infos['l'])
if done and not is_atari and args.verbose >= 1:
# NOTE: for env using VecNormalize, the mean reward
# is a normalized reward when `--norm_reward` flag is passed
print("Episode Reward: {:.2f}".format(running_reward))
print("Episode Length", ep_len)
running_reward = 0.0
ep_len = 0
# Workaround for https://github.com/openai/gym/issues/893
if not args.no_render:
if args.n_envs == 1 and not 'Bullet' in env_id and not is_atari:
# DummyVecEnv
# Unwrap env
while isinstance(env, VecNormalize) or isinstance(
env, VecFrameStack):
env = env.venv
env.envs[0].env.close()
else:
# SubprocVecEnv
env.close()
normalize = hyperparams['normalize']
if isinstance(normalize, str):
normalize_kwargs = eval(normalize)
normalize = True
del hyperparams['normalize']
# create the test env
env_hyperparams = {
'normalize': normalize,
'n_stack': n_stack,
'normalize_kwargs': normalize_kwargs
}
env = create_test_env(env_id,
n_envs=args.n_envs,
stats_path=params_path,
seed=seed,
hyperparams=env_hyperparams,
env_params=env_params,
should_render=False)
# load the pretrained agent
print("Loading pretrained agent")
model = ALGOS[algo].load(trained_agent_path, env=env, verbose=1)
# test the pretrained agent
print("=" * 10, "TESTING", env_id, "=" * 10)
if len(env_params):
print("environment parameters")
pprint(env_params)
ep_rewards = evaluate_model(model,
def main(): # noqa: C901
parser = argparse.ArgumentParser()
parser.add_argument("--env",
help="environment ID",
type=str,
default="CartPole-v1")
parser.add_argument("-f",
"--folder",
help="Log folder",
type=str,
default="rl-trained-agents")
parser.add_argument("--algo",
help="RL Algorithm",
default="ppo",
type=str,
required=False,
choices=list(ALGOS.keys()))
parser.add_argument("-n",
"--n-timesteps",
help="number of timesteps",
default=1000,
type=int)
parser.add_argument(
"--num-threads",
help="Number of threads for PyTorch (-1 to use default)",
default=-1,
type=int)
parser.add_argument("--n-envs",
help="number of environments",
default=1,
type=int)
parser.add_argument(
"--exp-id",
help="Experiment ID (default: 0: latest, -1: no exp folder)",
default=0,
type=int)
parser.add_argument("--verbose",
help="Verbose mode (0: no output, 1: INFO)",
default=1,
type=int)
parser.add_argument(
"--no-render",
action="store_true",
default=False,
help="Do not render the environment (useful for tests)")
parser.add_argument("--deterministic",
action="store_true",
default=False,
help="Use deterministic actions")
parser.add_argument(
"--load-best",
action="store_true",
default=False,
help="Load best model instead of last model if available")
parser.add_argument(
"--load-checkpoint",
type=int,
help="Load checkpoint instead of last model if available, "
"you must pass the number of timesteps corresponding to it",
)
parser.add_argument("--stochastic",
action="store_true",
default=False,
help="Use stochastic actions")
parser.add_argument(
"--norm-reward",
action="store_true",
default=False,
help="Normalize reward if applicable (trained with VecNormalize)")
parser.add_argument("--seed",
help="Random generator seed",
type=int,
default=0)
parser.add_argument("--reward-log",
help="Where to log reward",
default="",
type=str)
parser.add_argument(
"--gym-packages",
type=str,
nargs="+",
default=[],
help=
"Additional external Gym environment package modules to import (e.g. gym_minigrid)",
)
parser.add_argument(
"--env-kwargs",
type=str,
nargs="+",
action=StoreDict,
help="Optional keyword argument to pass to the env constructor")
args = parser.parse_args()
# Going through custom gym packages to let them register in the global registory
for env_module in args.gym_packages:
importlib.import_module(env_module)
env_id = args.env
algo = args.algo
folder = args.folder
if args.exp_id == 0:
args.exp_id = get_latest_run_id(os.path.join(folder, algo), env_id)
print(f"Loading latest experiment, id={args.exp_id}")
# Sanity checks
if args.exp_id > 0:
log_path = os.path.join(folder, algo, f"{env_id}_{args.exp_id}")
else:
log_path = os.path.join(folder, algo)
assert os.path.isdir(log_path), f"The {log_path} folder was not found"
found = False
for ext in ["zip"]:
model_path = os.path.join(log_path, f"{env_id}.{ext}")
found = os.path.isfile(model_path)
if found:
break
if args.load_best:
model_path = os.path.join(log_path, "best_model.zip")
found = os.path.isfile(model_path)
if args.load_checkpoint is not None:
model_path = os.path.join(
log_path, f"rl_model_{args.load_checkpoint}_steps.zip")
found = os.path.isfile(model_path)
if not found:
raise ValueError(
f"No model found for {algo} on {env_id}, path: {model_path}")
off_policy_algos = ["qrdqn", "dqn", "ddpg", "sac", "her", "td3", "tqc"]
if algo in off_policy_algos:
args.n_envs = 1
set_random_seed(args.seed)
if args.num_threads > 0:
if args.verbose > 1:
print(f"Setting torch.num_threads to {args.num_threads}")
th.set_num_threads(args.num_threads)
is_atari = ExperimentManager.is_atari(env_id)
stats_path = os.path.join(log_path, env_id)
hyperparams, stats_path = get_saved_hyperparams(
stats_path, norm_reward=args.norm_reward, test_mode=True)
# load env_kwargs if existing
env_kwargs = {}
args_path = os.path.join(log_path, env_id, "args.yml")
if os.path.isfile(args_path):
with open(args_path, "r") as f:
loaded_args = yaml.load(f, Loader=yaml.UnsafeLoader) # pytype: disable=module-attr
if loaded_args["env_kwargs"] is not None:
env_kwargs = loaded_args["env_kwargs"]
# overwrite with command line arguments
if args.env_kwargs is not None:
env_kwargs.update(args.env_kwargs)
log_dir = args.reward_log if args.reward_log != "" else None
print(env_kwargs)
env = create_test_env(
env_id,
n_envs=args.n_envs,
stats_path=stats_path,
seed=args.seed,
log_dir=log_dir,
should_render=not args.no_render,
hyperparams=hyperparams,
env_kwargs=env_kwargs,
)
kwargs = dict(seed=args.seed)
if algo in off_policy_algos:
# Dummy buffer size as we don't need memory to enjoy the trained agent
kwargs.update(dict(buffer_size=1))
# Check if we are running python 3.8+
# we need to patch saved model under python 3.6/3.7 to load them
newer_python_version = sys.version_info.major == 3 and sys.version_info.minor >= 8
custom_objects = {}
if newer_python_version:
custom_objects = {
"learning_rate": 0.0,
"lr_schedule": lambda _: 0.0,
"clip_range": lambda _: 0.0,
}
model = ALGOS[algo].load(model_path,
env=env,
custom_objects=custom_objects,
**kwargs)
obs = env.reset()
# Deterministic by default except for atari games
stochastic = args.stochastic or is_atari and not args.deterministic
deterministic = not stochastic
state = None
episode_reward = 0.0
episode_rewards, episode_lengths = [], []
ep_len = 0
# For HER, monitor success rate
successes = []
plt.figure(f"Enjoy {env_id}")
plt.title(f"{env_id}", fontsize=14)
plt.xlabel(f"Timesteps", fontsize=14)
# plt.ylabel("Score", fontsize=14)
observations = []
rewards = []
infos = []
try:
for _ in range(args.n_timesteps):
action, state = model.predict(obs,
state=state,
deterministic=deterministic)
obs, reward, done, info = env.step(action)
if not args.no_render:
env.render("human")
episode_reward += reward[0]
ep_len += 1
observations.append(obs)
rewards.append(reward)
infos.append(info[0].get("coating"))
if args.n_envs == 1:
# For atari the return reward is not the atari score
# so we have to get it from the infos dict
if is_atari and infos is not None and args.verbose >= 1:
episode_infos = infos[0].get("episode")
if episode_infos is not None:
print(f"Atari Episode Score: {episode_infos['r']:.2f}")
print("Atari Episode Length", episode_infos["l"])
if done and not is_atari and args.verbose > 0:
# NOTE: for env using VecNormalize, the mean reward
# is a normalized reward when `--norm_reward` flag is passed
print(f"Episode Reward: {episode_reward:.2f}")
print("Episode Length", ep_len)
episode_rewards.append(episode_reward)
episode_lengths.append(ep_len)
episode_reward = 0.0
ep_len = 0
state = None
# Reset also when the goal is achieved when using HER
if done and infos[0].get("is_success") is not None:
if args.verbose > 1:
print("Success?", infos[0].get("is_success", False))
if infos[0].get("is_success") is not None:
successes.append(infos[0].get("is_success", False))
episode_reward, ep_len = 0.0, 0
except KeyboardInterrupt:
pass
if args.verbose > 0 and len(successes) > 0:
print(f"Success rate: {100 * np.mean(successes):.2f}%")
if args.verbose > 0 and len(episode_rewards) > 0:
print(f"{len(episode_rewards)} Episodes")
print(
f"Mean reward: {np.mean(episode_rewards):.2f} +/- {np.std(episode_rewards):.2f}"
)
if args.verbose > 0 and len(episode_lengths) > 0:
print(
f"Mean episode length: {np.mean(episode_lengths):.2f} +/- {np.std(episode_lengths):.2f}"
)
env.close()
gesamt = 0
gesamt_mit = 0
for el in rewards:
if (el > 0):
gesamt += el
gesamt_mit += el
print(f"Gesamt reward: {gesamt}")
print(f"Gesamt reward mit: {gesamt_mit}")
plt.plot(np.arange(len(observations)),
rewards,
label="reward",
linewidth=1)
plt.plot(np.arange(len(observations)),
[obs[0][3] * 202 + 8 for obs in observations],
label="coating_dist",
linewidth=1)
plt.plot(np.arange(len(observations)),
[obs[0][1] * 202 + 8 for obs in observations],
label="coating_targets",
linewidth=1)
plt.plot(np.arange(len(observations)),
infos,
label="coating_real",
linewidth=1)
plt.plot(np.arange(len(observations)),
[obs[0][4] * 700 for obs in observations],
label="pressure",
linewidth=1)
plt.legend()
plt.show()
def main(): # noqa: C901
parser = argparse.ArgumentParser()
parser.add_argument(
"--env",
help="environment ID",
type=str,
default="CartPole-v1")
parser.add_argument(
"-f",
"--log-folder",
help="Log folder",
type=str,
default="rl-trained-agents")
parser.add_argument(
"--algo",
help="RL Algorithm",
default="ppo",
type=str,
required=False,
choices=list(ALGOS.keys()))
parser.add_argument(
"-n",
"--n-eval-steps",
help="Number of evaluation timesteps",
default=1000,
type=int)
parser.add_argument(
"--num-threads",
help="Number of threads for PyTorch (-1 to use default)",
default=-1,
type=int)
parser.add_argument(
"--n-envs",
help="number of environments",
default=1,
type=int)
parser.add_argument(
"--exp-id",
help="Experiment ID (default: 0: latest, -1: no exp folder)",
default=0,
type=int)
parser.add_argument(
"--verbose",
help="Verbose mode (0: no output, 1: INFO)",
default=1,
type=int)
parser.add_argument(
'--render',
help="1: Render environment, 0: don't render",
type=int,
choices=[0, 1],
default=0)
parser.add_argument(
'--deterministic',
help="1: Use deterministic actions, 0: Use stochastic actions",
type=int,
choices=[0, 1],
default=0)
parser.add_argument(
"--load-best",
action="store_true",
default=False,
help="Load best model instead of last model if available")
parser.add_argument(
"--load-checkpoint",
type=int,
help="Load checkpoint instead of last model if available, "
"you must pass the number of timesteps corresponding to it",
)
parser.add_argument(
"--stochastic",
action="store_true",
default=False,
help="Use stochastic actions (for DDPG/DQN/SAC)")
parser.add_argument(
"--norm-reward",
action="store_true",
default=False,
help="Normalize reward if applicable (trained with VecNormalize)")
parser.add_argument(
"--seed",
help="Random generator seed",
type=int,
default=0)
parser.add_argument(
"--reward-log",
help="Where to log reward",
default="",
type=str)
parser.add_argument(
"--gym-packages",
type=str,
nargs="+",
default=[],
help="Additional external Gym environemnt package modules to import (e.g. gym_minigrid)")
parser.add_argument(
"--env-kwargs",
type=str,
nargs="+",
action=StoreDict,
help="Optional keyword argument to pass to the env constructor")
parser.add_argument(
'--log-info',
help="1: Log information at each evaluation steps and save, 0: don't log",
type=int,
choices=[0, 1],
default=0)
parser.add_argument(
"--plot-dim",
help="Plot end effector and goal position in real time (0: Don't plot, 2: 2D (default), 3: 3D)",
type=int,
default=0,
choices=[0, 2, 3])
args = parser.parse_args()
#################################
# Prepare log if needed
if args.log_info:
log_df = pd.DataFrame()
log_dict = OrderedDict()
# Prepare plot if needed
if args.plot_dim == 2:
fig, (ax1, ax2) = plt.subplots(2, 1, sharey=True, figsize=(5, 10))
elif args.plot_dim == 3:
fig = plt.figure()
ax = fig.gca(projection='3d')
# Going through custom gym packages to let them register
# in the global registry
for env_module in args.gym_packages:
importlib.import_module(env_module)
env_id = args.env
algo = args.algo
folder = args.log_folder
if args.exp_id == 0:
args.exp_id = get_latest_run_id(os.path.join(folder, algo), env_id)
print(f"Loading latest experiment, id={args.exp_id}")
# Sanity checks
if args.exp_id > 0:
log_path = os.path.join(folder, algo, f"{env_id}_{args.exp_id}")
else:
log_path = os.path.join(folder, algo)
assert os.path.isdir(log_path), f"The {log_path} folder was not found"
found = False
for ext in ["zip"]:
model_path = os.path.join(log_path, f"{env_id}.{ext}")
found = os.path.isfile(model_path)
if found:
break
if args.load_best:
model_path = os.path.join(log_path, "best_model.zip")
found = os.path.isfile(model_path)
if args.load_checkpoint is not None:
model_path = os.path.join(
log_path, f"rl_model_{args.load_checkpoint}_steps.zip")
found = os.path.isfile(model_path)
if not found:
raise ValueError(
f"No model found for {algo} on {env_id}, path: {model_path}")
off_policy_algos = ["dqn", "ddpg", "sac", "her", "td3", "tqc"]
if algo in off_policy_algos:
args.n_envs = 1
set_random_seed(args.seed)
if args.num_threads > 0:
if args.verbose > 1:
print(f"Setting torch.num_threads to {args.num_threads}")
th.set_num_threads(args.num_threads)
stats_path = os.path.join(log_path, env_id)
hyperparams, stats_path = get_saved_hyperparams(
stats_path, norm_reward=args.norm_reward, test_mode=True)
# load env_kwargs if existing
env_kwargs = {}
args_path = os.path.join(log_path, env_id, "args.yml")
if os.path.isfile(args_path):
with open(args_path, "r") as f:
# pytype: disable=module-attr
loaded_args = yaml.load(f, Loader=yaml.UnsafeLoader)
if loaded_args["env_kwargs"] is not None:
env_kwargs = loaded_args["env_kwargs"]
# overwrite with command line arguments
if args.env_kwargs is not None:
env_kwargs.update(args.env_kwargs)
log_dir = args.reward_log if args.reward_log != "" else None
env = create_test_env(
env_id,
n_envs=args.n_envs,
stats_path=stats_path,
seed=args.seed,
log_dir=log_dir,
should_render=args.render,
hyperparams=hyperparams,
env_kwargs=env_kwargs,
)
kwargs = dict(seed=args.seed)
if algo in off_policy_algos:
# Dummy buffer size as we don't need memory to enjoy the trained agent
kwargs.update(dict(buffer_size=1))
model = ALGOS[algo].load(model_path, env=env, **kwargs)
obs = env.reset()
# Force deterministic for DQN, DDPG, SAC and HER (that is a wrapper around)
deterministic = args.deterministic or algo in off_policy_algos and not args.stochastic
state = None
episode_reward = 0.0
episode_rewards, episode_lengths = [], []
ep_len = 0
successes = [] # For HER, monitor success rate
episode_nb = 0
success_threshold_50 = 0.05
success_threshold_20 = 0.02
success_threshold_10 = 0.01
success_threshold_5 = 0.005
success_threshold_2 = 0.002
success_threshold_1 = 0.001
success_threshold_05 = 0.0005
ep_success_list_50 = []
ep_success_list_20 = []
ep_success_list_10 = []
ep_success_list_5 = []
ep_success_list_2 = []
ep_success_list_1 = []
ep_success_list_05 = []
success_list_50 = []
success_list_20 = []
success_list_10 = []
success_list_5 = []
success_list_2 = []
success_list_1 = []
success_list_05 = []
# Moved render flag outside the loop (Pierre)
if args.render:
env.render("human")
for t in range(args.n_eval_steps):
action, state = model.predict(
obs, state=state, deterministic=deterministic)
obs, reward, done, infos = env.step(action)
# Slow down simulation when rendering (Pierre)
if args.render:
if "widowx" in env_id:
time.sleep(1. / 30.)
else:
env.render()
if "widowx" in env_id:
# Update episode success list
ep_success_list_50 = calc_ep_success(
success_threshold_50, ep_success_list_50, infos)
ep_success_list_20 = calc_ep_success(
success_threshold_20, ep_success_list_20, infos)
ep_success_list_10 = calc_ep_success(
success_threshold_10, ep_success_list_10, infos)
ep_success_list_5 = calc_ep_success(
success_threshold_5, ep_success_list_5, infos)
ep_success_list_2 = calc_ep_success(
success_threshold_2, ep_success_list_2, infos)
ep_success_list_1 = calc_ep_success(
success_threshold_1, ep_success_list_1, infos)
ep_success_list_05 = calc_ep_success(
success_threshold_05, ep_success_list_05, infos)
episode_reward += reward[0]
ep_len += 1
# Real time plot
if args.plot_dim == 2:
goal = infos[0]['goal_position']
tip = infos[0]['tip_position']
ax1.cla()
ax1.plot(goal[0], goal[2], marker='o', color='g',
linestyle='', markersize=10, label="goal", alpha=0.5)
ax1.plot(tip[0], tip[2], marker='x', color='r',
linestyle='', markersize=10, label="end effector", mew=3)
circ_1_50 = plt.Circle(
(goal[0],
goal[2]),
radius=success_threshold_50,
edgecolor='g',
facecolor='w',
linestyle='--',
label="50 mm")
circ_1_20 = plt.Circle(
(goal[0],
goal[2]),
radius=success_threshold_20,
edgecolor='b',
facecolor='w',
linestyle='--',
label="20 mm")
circ_1_10 = plt.Circle(
(goal[0],
goal[2]),
radius=success_threshold_10,
edgecolor='m',
facecolor='w',
linestyle='--',
label="10 mm")
circ_1_5 = plt.Circle(
(goal[0],
goal[2]),
radius=success_threshold_5,
edgecolor='r',
facecolor='w',
linestyle='--',
label="5 mm")
ax1.add_patch(circ_1_50)
ax1.add_patch(circ_1_20)
ax1.add_patch(circ_1_10)
ax1.add_patch(circ_1_5)
ax1.set_xlim([-0.25, 0.25])
ax1.set_ylim([0, 0.5])
ax1.set_xlabel("x (m)", fontsize=15)
ax1.set_ylabel("z (m)", fontsize=15)
ax2.cla()
ax2.plot(goal[1], goal[2], marker='o', color='g',
linestyle='', markersize=10, alpha=0.5)
ax2.plot(
tip[1],
tip[2],
marker='x',
color='r',
linestyle='',
markersize=10,
mew=3)
circ_2_50 = plt.Circle(
(goal[1],
goal[2]),
radius=success_threshold_50,
edgecolor='g',
facecolor='w',
linestyle='--')
circ_2_20 = plt.Circle(
(goal[1],
goal[2]),
radius=success_threshold_20,
edgecolor='b',
facecolor='w',
linestyle='--')
circ_2_10 = plt.Circle(
(goal[1],
goal[2]),
radius=success_threshold_10,
edgecolor='m',
facecolor='w',
linestyle='--')
circ_2_5 = plt.Circle(
(goal[1],
goal[2]),
radius=success_threshold_5,
edgecolor='r',
facecolor='w',
linestyle='--')
ax2.add_patch(circ_2_50)
ax2.add_patch(circ_2_20)
ax2.add_patch(circ_2_10)
ax2.add_patch(circ_2_5)
ax2.set_xlim([-0.25, 0.25])
ax2.set_ylim([0, 0.5])
ax2.set_xlabel("y (m)", fontsize=15)
ax2.set_ylabel("z (m)", fontsize=15)
ax1.legend(loc='upper left', bbox_to_anchor=(
0, 1.2), ncol=3, fancybox=True, shadow=True)
fig.suptitle("timestep " + str(ep_len) + " | distance to target: " +
str(round(infos[0]['new_distance'] * 1000, 1)) + " mm")
plt.pause(0.01)
# plt.show()
elif args.plot_dim == 3:
goal = infos[0]['goal_position']
tip = infos[0]['tip_position']
ax.cla()
ax.plot([goal[0]], [goal[1]], zs=[goal[2]], marker='o',
color='g', linestyle='', markersize=10, alpha=0.5)
ax.plot([tip[0]], [tip[1]], zs=[tip[2]], marker='x',
color='r', linestyle='', markersize=10, mew=3)
ax.set_xlim([-0.2, 0.2])
ax.set_ylim([-0.2, 0.2])
ax.set_zlim([0, 0.5])
ax.set_xlabel("x (m)", fontsize=15)
ax.set_ylabel("y (m)", fontsize=15)
ax.set_zlabel("z (m)", fontsize=15)
fig.suptitle("timestep " + str(ep_len) + " | distance to target: " +
str(round(infos[0]['new_distance'] * 1000, 1)) + " mm")
plt.pause(0.01)
# plt.show()
if args.log_info:
log_dict['episode'] = episode_nb
log_dict['timestep'] = t
log_dict['action_1'] = action[0][0]
log_dict['action_2'] = action[0][1]
log_dict['action_3'] = action[0][2]
log_dict['action_4'] = action[0][3]
log_dict['action_5'] = action[0][4]
log_dict['action_6'] = action[0][5]
log_dict['old_joint_pos_1'] = infos[0]['old_joint_pos'][0]
log_dict['old_joint_pos_2'] = infos[0]['old_joint_pos'][1]
log_dict['old_joint_pos_3'] = infos[0]['old_joint_pos'][2]
log_dict['old_joint_pos_4'] = infos[0]['old_joint_pos'][3]
log_dict['old_joint_pos_5'] = infos[0]['old_joint_pos'][4]
log_dict['old_joint_pos_6'] = infos[0]['old_joint_pos'][5]
log_dict['new_joint_pos_1'] = infos[0]['new_joint_pos'][0]
log_dict['new_joint_pos_2'] = infos[0]['new_joint_pos'][1]
log_dict['new_joint_pos_3'] = infos[0]['new_joint_pos'][2]
log_dict['new_joint_pos_4'] = infos[0]['new_joint_pos'][3]
log_dict['new_joint_pos_5'] = infos[0]['new_joint_pos'][4]
log_dict['new_joint_pos_6'] = infos[0]['new_joint_pos'][5]
log_dict['joint_vel_1'] = infos[0]['joint_vel'][0]
log_dict['joint_vel_2'] = infos[0]['joint_vel'][1]
log_dict['joint_vel_3'] = infos[0]['joint_vel'][2]
log_dict['joint_vel_4'] = infos[0]['joint_vel'][3]
log_dict['joint_vel_5'] = infos[0]['joint_vel'][4]
log_dict['joint_vel_6'] = infos[0]['joint_vel'][5]
log_dict['joint1_min'] = -3.1
log_dict['joint1_max'] = 3.1
log_dict['joint2_min'] = -1.571
log_dict['joint2_max'] = 1.571
log_dict['joint3_min'] = -1.571
log_dict['joint3_max'] = 1.571
log_dict['joint4_min'] = -1.745
log_dict['joint4_max'] = 1.745
log_dict['joint5_min'] = -2.617
log_dict['joint5_max'] = 2.617
log_dict['joint6_min'] = 0.003
log_dict['joint6_max'] = 0.03
log_dict['action_low1'] = env.action_space.low[0]
log_dict['action_low2'] = env.action_space.low[1]
log_dict['action_low3'] = env.action_space.low[2]
log_dict['action_low4'] = env.action_space.low[3]
log_dict['action_low5'] = env.action_space.low[4]
log_dict['action_low6'] = env.action_space.low[5]
log_dict['action_high1'] = env.action_space.high[0]
log_dict['action_high2'] = env.action_space.high[1]
log_dict['action_high3'] = env.action_space.high[2]
log_dict['action_high4'] = env.action_space.high[3]
log_dict['action_high5'] = env.action_space.high[4]
log_dict['action_high6'] = env.action_space.high[5]
log_dict['reward'] = reward[0]
log_dict['return'] = episode_reward
log_dict['new_distance'] = infos[0]['new_distance']
log_dict['old_distance'] = infos[0]['old_distance']
log_dict['target_x'] = infos[0]['goal_position'][0]
log_dict['target_y'] = infos[0]['goal_position'][1]
log_dict['target_z'] = infos[0]['goal_position'][2]
log_dict['tip_y'] = infos[0]['tip_position'][1]
log_dict['tip_x'] = infos[0]['tip_position'][0]
log_dict['tip_z'] = infos[0]['tip_position'][2]
log_dict['done'] = done[0]
# log_dict['obs'] = obs
# log_dict['obs_space_low'] = env.observation_space.low
# log_dict['obs_space_high'] = env.observation_space.high
log_df = log_df.append(log_dict, ignore_index=True)
if args.n_envs == 1:
if done and args.verbose > 0:
# NOTE: for env using VecNormalize, the mean reward
# is a normalized reward when `--norm_reward` flag is passed
# print(f"Episode Reward: {episode_reward:.2f}") # commented by Pierre
# print("Episode Length", ep_len) # commented by Pierre
episode_rewards.append(episode_reward)
episode_lengths.append(ep_len)
episode_nb += 1
if "widowx" in env_id:
# append the last element of the episode success list when
# episode is done
success_list_50 = calc_success_list(
ep_success_list_50, success_list_50)
success_list_20 = calc_success_list(
ep_success_list_20, success_list_20)
success_list_10 = calc_success_list(
ep_success_list_10, success_list_10)
success_list_5 = calc_success_list(
ep_success_list_5, success_list_5)
success_list_2 = calc_success_list(
ep_success_list_2, success_list_2)
success_list_1 = calc_success_list(
ep_success_list_1, success_list_1)
success_list_05 = calc_success_list(
ep_success_list_05, success_list_05)
# If the episode is successful and it starts from an
# unsucessful step, calculate reach time
reachtime_list_50 = calc_reach_time(ep_success_list_50)
reachtime_list_20 = calc_reach_time(ep_success_list_20)
reachtime_list_10 = calc_reach_time(ep_success_list_10)
reachtime_list_5 = calc_reach_time(ep_success_list_5)
reachtime_list_2 = calc_reach_time(ep_success_list_2)
reachtime_list_1 = calc_reach_time(ep_success_list_1)
reachtime_list_05 = calc_reach_time(ep_success_list_05)
if args.log_info:
log_df = log_df[log_dict.keys()] # sort columns
# add estimated tip velocity and acceleration (according to
# the documentation, 1 timestep = 240 Hz)
log_df['est_vel'] = log_df['new_distance'].diff() * 240
log_df['est_vel'].loc[0] = 0 # initial velocity is 0
log_df['est_acc'] = log_df['est_vel'].diff() * 240
log_df['est_acc'].loc[0] = 0 # initial acceleration is 0
log_df.to_csv(
log_path +
"/res_episode_" +
str(episode_nb) +
".csv",
index=False) # slow
# log_df.to_pickle(log_path+"/res_episode_"+str(episode)+".pkl")
# # fast
# Reset for the new episode
episode_reward = 0.0
ep_len = 0
state = None
ep_success_list_50 = []
ep_success_list_20 = []
ep_success_list_10 = []
ep_success_list_5 = []
ep_success_list_2 = []
ep_success_list_1 = []
ep_success_list_05 = []
# Reset also when the goal is achieved when using HER
if done and infos[0].get("is_success") is not None:
if args.verbose > 1:
print("Success?", infos[0].get("is_success", False))
# Alternatively, you can add a check to wait for the end of the
# episode
if done:
obs = env.reset()
if infos[0].get("is_success") is not None:
successes.append(infos[0].get("is_success", False))
episode_reward, ep_len = 0.0, 0
if args.verbose > 0 and len(successes) > 0:
print(f"Success rate: {100 * np.mean(successes):.2f}%")
if args.verbose > 0 and len(episode_lengths) > 0:
print(
f"Mean episode length: {np.mean(episode_lengths):.2f} +/- {np.std(episode_lengths):.2f}")
if args.verbose > 0 and len(episode_rewards) > 0:
print(
f"Mean reward: {np.mean(episode_rewards):.2f} +/- {np.std(episode_rewards):.2f}")
if "widowx" in env_id:
SR_mean_50, RT_mean_50 = calc_mean_successratio_reachtime(
success_threshold_50, success_list_50, reachtime_list_50)
SR_mean_20, RT_mean_20 = calc_mean_successratio_reachtime(
success_threshold_20, success_list_20, reachtime_list_20)
SR_mean_10, RT_mean_10 = calc_mean_successratio_reachtime(
success_threshold_10, success_list_10, reachtime_list_10)
SR_mean_5, RT_mean_5 = calc_mean_successratio_reachtime(
success_threshold_5, success_list_5, reachtime_list_5)
SR_mean_2, RT_mean_2 = calc_mean_successratio_reachtime(
success_threshold_2, success_list_2, reachtime_list_2)
SR_mean_1, RT_mean_1 = calc_mean_successratio_reachtime(
success_threshold_1, success_list_1, reachtime_list_1)
SR_mean_05, RT_mean_05 = calc_mean_successratio_reachtime(
success_threshold_05, success_list_05, reachtime_list_05)
# log metrics to stats.csv
d = {
"Eval mean reward": np.mean(episode_rewards),
"Eval std": np.std(episode_rewards),
"success ratio 50mm": SR_mean_50,
"Average reach time 50mm": RT_mean_50,
"success ratio 20mm": SR_mean_20,
"Average reach time 20mm": RT_mean_20,
"success ratio 10mm": SR_mean_10,
"Average reach time 10mm": RT_mean_10,
"success ratio 5mm": SR_mean_5,
"Average reach time 5mm": RT_mean_5,
"success ratio 2mm": SR_mean_2,
"Average reach time 2mm": RT_mean_2,
"success ratio 1mm": SR_mean_1,
"Average reach time 1mm": RT_mean_1,
"success ratio 0.5mm": SR_mean_05,
"Average reach time 0.5mm": RT_mean_05
}
# print("path:", log_path)
df = pd.DataFrame(d, index=[0])
df.to_csv(log_path + "/stats.csv", index=False)
# Workaround for https://github.com/openai/gym/issues/893
if args.render:
if args.n_envs == 1 and "Bullet" not in env_id and isinstance(
env, VecEnv):
# DummyVecEnv
# Unwrap env
while isinstance(env, VecEnvWrapper):
env = env.venv
if isinstance(env, DummyVecEnv):
env.envs[0].env.close()
else:
env.close()
else:
# SubprocVecEnv
env.close()
def main(): # noqa: C901
parser = argparse.ArgumentParser()
parser.add_argument("--env",
help="environment ID",
type=str,
default="CartPole-v1")
parser.add_argument("-f",
"--folder",
help="Log folder",
type=str,
default="rl-trained-agents")
parser.add_argument("--algo",
help="RL Algorithm",
default="ppo",
type=str,
required=False,
choices=list(ALGOS.keys()))
parser.add_argument("-n",
"--n-timesteps",
help="number of timesteps",
default=300,
type=int)
parser.add_argument(
"--num-threads",
help="Number of threads for PyTorch (-1 to use default)",
default=-1,
type=int)
parser.add_argument("--n-envs",
help="number of environments",
default=1,
type=int)
parser.add_argument(
"--exp-id",
help="Experiment ID (default: 0: latest, -1: no exp folder)",
default=0,
type=int)
parser.add_argument("--verbose",
help="Verbose mode (0: no output, 1: INFO)",
default=1,
type=int)
parser.add_argument(
"--no-render",
action="store_true",
default=False,
help="Do not render the environment (useful for tests)")
parser.add_argument("--deterministic",
action="store_true",
default=False,
help="Use deterministic actions")
parser.add_argument(
"--load-best",
action="store_true",
default=False,
help="Load best model instead of last model if available")
parser.add_argument(
"--load-checkpoint",
type=int,
help="Load checkpoint instead of last model if available, "
"you must pass the number of timesteps corresponding to it",
)
parser.add_argument("--stochastic",
action="store_true",
default=False,
help="Use stochastic actions")
parser.add_argument(
"--norm-reward",
action="store_true",
default=False,
help="Normalize reward if applicable (trained with VecNormalize)")
parser.add_argument("--seed",
help="Random generator seed",
type=int,
default=0)
parser.add_argument("--reward-log",
help="Where to log reward",
default="",
type=str)
parser.add_argument(
"--gym-packages",
type=str,
nargs="+",
default=[],
help=
"Additional external Gym environemnt package modules to import (e.g. gym_minigrid)",
)
parser.add_argument(
"--env-kwargs",
type=str,
nargs="+",
action=StoreDict,
help="Optional keyword argument to pass to the env constructor")
args = parser.parse_args()
# Going through custom gym packages to let them register in the global registory
for env_module in args.gym_packages:
importlib.import_module(env_module)
env_id = args.env
algo = args.algo
folder = args.folder
if args.exp_id == 0:
args.exp_id = get_latest_run_id(os.path.join(folder, algo), env_id)
print(f"Loading latest experiment, id={args.exp_id}")
# Sanity checks
if args.exp_id > 0:
log_path = os.path.join(folder, algo, f"{env_id}_{args.exp_id}")
else:
log_path = os.path.join(folder, algo)
assert os.path.isdir(log_path), f"The {log_path} folder was not found"
found = False
for ext in ["zip"]:
model_path = os.path.join(log_path, f"{env_id}.{ext}")
found = os.path.isfile(model_path)
if found:
break
if args.load_best:
model_path = os.path.join(log_path, "best_model.zip")
found = os.path.isfile(model_path)
if args.load_checkpoint is not None:
model_path = os.path.join(
log_path, f"rl_model_{args.load_checkpoint}_steps.zip")
found = os.path.isfile(model_path)
if not found:
raise ValueError(
f"No model found for {algo} on {env_id}, path: {model_path}")
off_policy_algos = ["qrdqn", "dqn", "ddpg", "sac", "her", "td3", "tqc"]
if algo in off_policy_algos:
args.n_envs = 1
set_random_seed(args.seed)
if args.num_threads > 0:
if args.verbose > 1:
print(f"Setting torch.num_threads to {args.num_threads}")
th.set_num_threads(args.num_threads)
is_atari = ExperimentManager.is_atari(env_id)
stats_path = os.path.join(log_path, env_id)
hyperparams, stats_path = get_saved_hyperparams(
stats_path, norm_reward=args.norm_reward, test_mode=True)
# load env_kwargs if existing
env_kwargs = {}
args_path = os.path.join(log_path, env_id, "args.yml")
if os.path.isfile(args_path):
with open(args_path, "r") as f:
loaded_args = yaml.load(f, Loader=yaml.UnsafeLoader) # pytype: disable=module-attr
if loaded_args["env_kwargs"] is not None:
env_kwargs = loaded_args["env_kwargs"]
# overwrite with command line arguments
if args.env_kwargs is not None:
env_kwargs.update(args.env_kwargs)
log_dir = args.reward_log if args.reward_log != "" else None
env = create_test_env(
env_id,
n_envs=args.n_envs,
stats_path=stats_path,
seed=args.seed,
log_dir=log_dir,
should_render=not args.no_render,
hyperparams=hyperparams,
env_kwargs=env_kwargs,
)
kwargs = dict(seed=args.seed)
if algo in off_policy_algos:
# Dummy buffer size as we don't need memory to enjoy the trained agent
kwargs.update(dict(buffer_size=1))
model = ALGOS[algo].load(model_path, env=env, **kwargs)
obs = env.reset()
# The following 3 variables are used to create GIFS (the images stacked to create a GIF are those acquired by the vision sensor)
images = []
obs = env.reset()
img = env.render(mode='rgb')
# Deterministic by default except for atari games
stochastic = args.stochastic or is_atari and not args.deterministic
deterministic = not stochastic
state = None
episode_reward = 0.0
episode_rewards, episode_lengths = [], []
ep_len = 0
successes = []
for _ in range(args.n_timesteps):
images.append(img)
action, state = model.predict(obs,
state=state,
deterministic=deterministic)
obs, reward, done, infos = env.step(action)
img = model.env.render(mode='rgb')
if not args.no_render:
env.render("human")
episode_reward += reward[0]
ep_len += 1
if args.n_envs == 1:
# For atari the return reward is not the atari score
# so we have to get it from the infos dict
if is_atari and infos is not None and args.verbose >= 1:
episode_infos = infos[0].get('episode')
if episode_infos is not None:
print("Atari Episode Score: {:.2f}".format(
episode_infos['r']))
print("Atari Episode Length", episode_infos['l'])
if done and not is_atari and args.verbose > 0:
# NOTE: for env using VecNormalize, the mean reward
# is a normalized reward when `--norm_reward` flag is passed
print("Episode Reward: {:.2f}".format(episode_reward))
print("Episode Length", ep_len)
state = None
episode_rewards.append(episode_reward)
episode_lengths.append(ep_len)
episode_reward = 0.0
ep_len = 0
# Reset also when the goal is achieved when using HER
if done:
print("Success?", infos[0].get('is_success', False))
# Alternatively, you can add a check to wait for the end of the episode
if done:
obs = env.reset()
successes.append(infos[0].get('is_success', False))
episode_reward = 0.0
ep_len = 0
# Creation of a GIF and saving of rewards and successes in a csv file :
imageio.mimsave(
'dVRL_10ep.gif',
[np.array(img) for i, img in enumerate(images) if i % 2 == 0],
fps=5)
savetxt('ep_rewards.csv', episode_rewards, delimiter=',')
savetxt('ep_success.csv', successes, delimiter=',')
if args.verbose > 0 and len(successes) > 0:
print(f"Success rate: {100 * np.mean(successes):.2f}%")
if args.verbose > 0 and len(episode_rewards) > 0:
print(
f"Mean reward: {np.mean(episode_rewards):.2f} +/- {np.std(episode_rewards):.2f}"
)
if args.verbose > 0 and len(episode_lengths) > 0:
print(
f"Mean episode length: {np.mean(episode_lengths):.2f} +/- {np.std(episode_lengths):.2f}"
)
# Workaround for https://github.com/openai/gym/issues/893
if not args.no_render:
if args.n_envs == 1 and "Bullet" not in env_id and not is_atari and isinstance(
env, VecEnv):
# DummyVecEnv
# Unwrap env
while isinstance(env, VecEnvWrapper):
env = env.venv
if isinstance(env, DummyVecEnv):
env.envs[0].env.close()
else:
env.close()
else:
# SubprocVecEnv
env.close()
def main(): # noqa: C901
parser = argparse.ArgumentParser()
parser.add_argument('--env',
help='environment ID',
type=str,
default='CartPole-v1')
parser.add_argument('-f',
'--folder',
help='Log folder',
type=str,
default='rl-trained-agents')
parser.add_argument('--algo',
help='RL Algorithm',
default='ppo',
type=str,
required=False,
choices=list(ALGOS.keys()))
parser.add_argument('-n',
'--n-timesteps',
help='number of timesteps',
default=1000,
type=int)
parser.add_argument(
'--num-threads',
help='Number of threads for PyTorch (-1 to use default)',
default=-1,
type=int)
parser.add_argument('--n-envs',
help='number of environments',
default=1,
type=int)
parser.add_argument(
'--exp-id',
help='Experiment ID (default: 0: latest, -1: no exp folder)',
default=0,
type=int)
parser.add_argument('--verbose',
help='Verbose mode (0: no output, 1: INFO)',
default=1,
type=int)
parser.add_argument(
'--no-render',
action='store_true',
default=False,
help='Do not render the environment (useful for tests)')
parser.add_argument('--deterministic',
action='store_true',
default=False,
help='Use deterministic actions')
parser.add_argument(
'--load-best',
action='store_true',
default=False,
help='Load best model instead of last model if available')
parser.add_argument(
'--load-checkpoint',
type=int,
help='Load checkpoint instead of last model if available, '
'you must pass the number of timesteps corresponding to it')
parser.add_argument('--stochastic',
action='store_true',
default=False,
help='Use stochastic actions (for DDPG/DQN/SAC)')
parser.add_argument(
'--norm-reward',
action='store_true',
default=False,
help='Normalize reward if applicable (trained with VecNormalize)')
parser.add_argument('--seed',
help='Random generator seed',
type=int,
default=0)
parser.add_argument('--reward-log',
help='Where to log reward',
default='',
type=str)
parser.add_argument(
'--gym-packages',
type=str,
nargs='+',
default=[],
help=
'Additional external Gym environemnt package modules to import (e.g. gym_minigrid)'
)
parser.add_argument(
'--env-kwargs',
type=str,
nargs='+',
action=StoreDict,
help='Optional keyword argument to pass to the env constructor')
args = parser.parse_args()
# Going through custom gym packages to let them register in the global registory
for env_module in args.gym_packages:
importlib.import_module(env_module)
env_id = args.env
algo = args.algo
folder = args.folder
if args.exp_id == 0:
args.exp_id = get_latest_run_id(os.path.join(folder, algo), env_id)
print('Loading latest experiment, id={}'.format(args.exp_id))
# Sanity checks
if args.exp_id > 0:
log_path = os.path.join(folder, algo,
'{}_{}'.format(env_id, args.exp_id))
else:
log_path = os.path.join(folder, algo)
assert os.path.isdir(log_path), f"The {log_path} folder was not found"
found = False
for ext in ['zip']:
model_path = os.path.join(log_path, f'{env_id}.{ext}')
found = os.path.isfile(model_path)
if found:
break
if args.load_best:
model_path = os.path.join(log_path, "best_model.zip")
found = os.path.isfile(model_path)
if args.load_checkpoint is not None:
model_path = os.path.join(
log_path, f"rl_model_{args.load_checkpoint}_steps.zip")
found = os.path.isfile(model_path)
if not found:
raise ValueError(
f"No model found for {algo} on {env_id}, path: {model_path}")
if algo in ['dqn', 'ddpg', 'sac', 'td3']:
args.n_envs = 1
set_random_seed(args.seed)
if args.num_threads > 0:
if args.verbose > 1:
print(f"Setting torch.num_threads to {args.num_threads}")
th.set_num_threads(args.num_threads)
is_atari = 'NoFrameskip' in env_id
stats_path = os.path.join(log_path, env_id)
hyperparams, stats_path = get_saved_hyperparams(
stats_path, norm_reward=args.norm_reward, test_mode=True)
env_kwargs = {} if args.env_kwargs is None else args.env_kwargs
log_dir = args.reward_log if args.reward_log != '' else None
env = create_test_env(env_id,
n_envs=args.n_envs,
stats_path=stats_path,
seed=args.seed,
log_dir=log_dir,
should_render=not args.no_render,
hyperparams=hyperparams,
env_kwargs=env_kwargs)
model = ALGOS[algo].load(model_path, env=env)
obs = env.reset()
# Force deterministic for DQN, DDPG, SAC and HER (that is a wrapper around)
deterministic = args.deterministic or algo in [
'dqn', 'ddpg', 'sac', 'her', 'td3'
] and not args.stochastic
state = None
episode_reward = 0.0
episode_rewards, episode_lengths = [], []
ep_len = 0
# For HER, monitor success rate
successes = []
for _ in range(args.n_timesteps):
action, state = model.predict(obs,
state=state,
deterministic=deterministic)
# Random Agent
# action = [env.action_space.sample()]
# Clip Action to avoid out of bound errors
if isinstance(env.action_space, gym.spaces.Box):
action = np.clip(action, env.action_space.low,
env.action_space.high)
obs, reward, done, infos = env.step(action)
if not args.no_render:
env.render('human')
episode_reward += reward[0]
ep_len += 1
if args.n_envs == 1:
# For atari the return reward is not the atari score
# so we have to get it from the infos dict
if is_atari and infos is not None and args.verbose >= 1:
episode_infos = infos[0].get('episode')
if episode_infos is not None:
print(f"Atari Episode Score: {episode_infos['r']:.2f}")
print("Atari Episode Length", episode_infos['l'])
if done and not is_atari and args.verbose > 0:
# NOTE: for env using VecNormalize, the mean reward
# is a normalized reward when `--norm_reward` flag is passed
print(f"Episode Reward: {episode_reward:.2f}")
print("Episode Length", ep_len)
episode_rewards.append(episode_reward)
episode_lengths.append(ep_len)
episode_reward = 0.0
ep_len = 0
state = None
# Reset also when the goal is achieved when using HER
if done and infos[0].get('is_success') is not None:
if args.verbose > 1:
print("Success?", infos[0].get('is_success', False))
# Alternatively, you can add a check to wait for the end of the episode
if done:
obs = env.reset()
if infos[0].get('is_success') is not None:
successes.append(infos[0].get('is_success', False))
episode_reward, ep_len = 0.0, 0
if args.verbose > 0 and len(successes) > 0:
print("Success rate: {:.2f}%".format(100 * np.mean(successes)))
if args.verbose > 0 and len(episode_rewards) > 0:
print("Mean reward: {:.2f} +/- {:.2f}".format(np.mean(episode_rewards),
np.std(episode_rewards)))
if args.verbose > 0 and len(episode_lengths) > 0:
print("Mean episode length: {:.2f} +/- {:.2f}".format(
np.mean(episode_lengths), np.std(episode_lengths)))
# Workaround for https://github.com/openai/gym/issues/893
if not args.no_render:
if (args.n_envs == 1 and 'Bullet' not in env_id and not is_atari
and isinstance(env, VecEnv)):
# DummyVecEnv
# Unwrap env
while isinstance(env, VecEnvWrapper):
env = env.venv
if isinstance(env, DummyVecEnv):
env.envs[0].env.close()
else:
env.close()
else:
# SubprocVecEnv
env.close()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env',
help='environment ID',
type=str,
default='CartPole-v1')
parser.add_argument('-f',
'--folder',
help='Log folder',
type=str,
default='trained_agents')
parser.add_argument('--algo',
help='RL Algorithm',
default='ppo2',
type=str,
required=False,
choices=list(ALGOS.keys()))
parser.add_argument('-n',
'--n-timesteps',
help='number of timesteps',
default=1000,
type=int)
parser.add_argument('--n-envs',
help='number of environments',
default=1,
type=int)
parser.add_argument(
'--exp-id',
help='Experiment ID (default: -1, no exp folder, 0: latest)',
default=-1,
type=int)
parser.add_argument('--verbose',
help='Verbose mode (0: no output, 1: INFO)',
default=1,
type=int)
parser.add_argument(
'--no-render',
action='store_true',
default=False,
help='Do not render the environment (useful for tests)')
parser.add_argument('--deterministic',
action='store_true',
default=False,
help='Use deterministic actions')
parser.add_argument('--stochastic',
action='store_true',
default=False,
help='Use stochastic actions (for DDPG/DQN/SAC)')
parser.add_argument(
'--load-best',
action='store_true',
default=False,
help='Load best model instead of last model if available')
parser.add_argument(
'--norm-reward',
action='store_true',
default=False,
help='Normalize reward if applicable (trained with VecNormalize)')
parser.add_argument('--seed',
help='Random generator seed',
type=int,
default=0)
parser.add_argument('--reward-log',
help='Where to log reward',
default='',
type=str)
parser.add_argument(
'--gym-packages',
type=str,
nargs='+',
default=[],
help=
'Additional external Gym environemnt package modules to import (e.g. gym_minigrid)'
)
parser.add_argument(
'--env-kwargs',
type=str,
nargs='+',
action=StoreDict,
help='Optional keyword argument to pass to the env constructor')
parser.add_argument('--render-pybullet',
help='Slow down Pybullet simulation to render',
default=False) # added by Pierre
parser.add_argument('--random-pol', help='Random policy',
default=False) # added by Pierre
parser.add_argument(
'--log-dir-random',
help='Log directory of the random policy') # added by Pierre
args = parser.parse_args()
# Going through custom gym packages to let them register in the global registory
for env_module in args.gym_packages:
importlib.import_module(env_module)
env_id = args.env
algo = args.algo
folder = args.folder
if args.exp_id == 0:
args.exp_id = get_latest_run_id(os.path.join(folder, algo), env_id)
print('Loading latest experiment, id={}'.format(args.exp_id))
# Sanity checks
if args.exp_id > 0:
log_path = os.path.join(folder, algo,
'{}_{}'.format(env_id, args.exp_id))
else:
log_path = os.path.join(folder, algo)
assert os.path.isdir(log_path), "The {} folder was not found".format(
log_path)
if not args.random_pol: # added by Pierre
model_path = find_saved_model(algo,
log_path,
env_id,
load_best=args.load_best)
if algo in ['dqn', 'ddpg', 'sac', 'td3']:
args.n_envs = 1
set_global_seeds(args.seed)
is_atari = 'NoFrameskip' in env_id
stats_path = os.path.join(log_path, env_id)
hyperparams, stats_path = get_saved_hyperparams(
stats_path, norm_reward=args.norm_reward, test_mode=True)
log_dir = args.reward_log if args.reward_log != '' else None
env_kwargs = {} if args.env_kwargs is None else args.env_kwargs
env = create_test_env(env_id,
n_envs=args.n_envs,
is_atari=is_atari,
stats_path=stats_path,
seed=args.seed,
log_dir=log_dir,
should_render=not args.no_render,
hyperparams=hyperparams,
env_kwargs=env_kwargs)
# ACER raises errors because the environment passed must have
# the same number of environments as the model was trained on.
load_env = None if algo == 'acer' else env
if not args.random_pol: # added by Pierre
model = ALGOS[algo].load(model_path, env=load_env)
obs = env.reset()
# Force deterministic for DQN, DDPG, SAC and HER (that is a wrapper around)
deterministic = args.deterministic or algo in [
'dqn', 'ddpg', 'sac', 'her', 'td3'
] and not args.stochastic
# INITIALISE METRICS
episode_reward = 0.0
episode_rewards, episode_lengths = [], []
ep_len = 0
success_threshold_50 = 0.05
success_list_50, reachtime_list_50, episode_success_list_50 = [], [], []
success_threshold_20 = 0.02
success_list_20, reachtime_list_20, episode_success_list_20 = [], [], []
success_threshold_10 = 0.01
success_list_10, reachtime_list_10, episode_success_list_10 = [], [], []
success_threshold_5 = 0.005
success_list_5, reachtime_list_5, episode_success_list_5 = [], [], []
# For HER, monitor success rate
successes = []
state = None
for _ in range(args.n_timesteps):
# Added by Pierre
if args.random_pol:
action = [env.action_space.sample()] # Random Agent
else:
action, state = model.predict(obs,
state=state,
deterministic=deterministic)
# Clip Action to avoid out of bound errors
if isinstance(env.action_space, gym.spaces.Box):
action = np.clip(action, env.action_space.low,
env.action_space.high)
obs, reward, done, infos = env.step(action)
# if args.render_pybullet:
# time.sleep(1./30.) # added by Pierre (slow down Pybullet for rendering)
# added by Pierre
if infos[0]['dist_ft_t'] <= success_threshold_50:
episode_success_list_50.append(1)
else:
episode_success_list_50.append(0)
if infos[0]['dist_ft_t'] <= success_threshold_20:
episode_success_list_20.append(1)
else:
episode_success_list_20.append(0)
if infos[0]['dist_ft_t'] <= success_threshold_10:
episode_success_list_10.append(1)
else:
episode_success_list_10.append(0)
if infos[0]['dist_ft_t'] <= success_threshold_5:
episode_success_list_5.append(1)
else:
episode_success_list_5.append(0)
if not args.no_render:
env.render('human')
# env.render(mode="human")
episode_reward += reward[0]
ep_len += 1
if args.n_envs == 1:
# For atari the return reward is not the atari score
# so we have to get it from the infos dict
if is_atari and infos is not None and args.verbose >= 1:
episode_infos = infos[0].get('episode')
if episode_infos is not None:
print("Atari Episode Score: {:.2f}".format(
episode_infos['r']))
print("Atari Episode Length", episode_infos['l'])
if done and not is_atari and args.verbose > 0:
# NOTE: for env using VecNormalize, the mean reward
# is a normalized reward when `--norm_reward` flag is passed
print("Episode Reward: {:.2f}".format(episode_reward))
print("Episode Length", ep_len)
episode_rewards.append(episode_reward)
episode_lengths.append(ep_len)
# Pierre: append the last element of the episode success list when episode is done
success_list_50.append(episode_success_list_50[-1])
success_list_20.append(episode_success_list_20[-1])
success_list_10.append(episode_success_list_10[-1])
success_list_5.append(episode_success_list_5[-1])
# if the episode is successful and it starts from an unsucessful step, calculate reach time
if episode_success_list_50[
-1] == True and episode_success_list_50[0] == False:
idx = 0
while episode_success_list_50[idx] == False:
idx += 1
reachtime_list_50.append(idx)
if episode_success_list_20[
-1] == True and episode_success_list_20[0] == False:
idx = 0
while episode_success_list_20[idx] == False:
idx += 1
reachtime_list_20.append(idx)
if episode_success_list_10[
-1] == True and episode_success_list_10[0] == False:
idx = 0
while episode_success_list_10[idx] == False:
idx += 1
reachtime_list_10.append(idx)
if episode_success_list_5[
-1] == True and episode_success_list_5[0] == False:
idx = 0
while episode_success_list_5[idx] == False:
idx += 1
reachtime_list_5.append(idx)
# RESET FOR NEW EPISODE
state = None
episode_reward = 0.0
ep_len = 0
episode_success_list_50 = []
episode_success_list_20 = []
episode_success_list_10 = []
episode_success_list_5 = []
# Reset also when the goal is achieved when using HER
if done or infos[0].get('is_success', False):
if args.algo == 'her' and args.verbose > 1:
print("Success?", infos[0].get('is_success', False))
# Alternatively, you can add a check to wait for the end of the episode
# if done:
obs = env.reset()
if args.algo == 'her':
successes.append(infos[0].get('is_success', False))
episode_reward, ep_len = 0.0, 0
if args.verbose > 0 and len(successes) > 0:
print("Success rate: {:.2f}%".format(100 * np.mean(successes)))
if args.verbose > 0 and len(episode_rewards) > 0:
print("Mean reward: {:.2f} +/- {:.2f}".format(np.mean(episode_rewards),
np.std(episode_rewards)))
print(
"success threshold: {} | success ratio: {:.2f} | Average reach time: {:.2f}"
.format(success_threshold_50, np.mean(success_list_50),
np.mean(reachtime_list_50)))
print(
"success threshold: {} | success ratio: {:.2f} | Average reach time: {:.2f}"
.format(success_threshold_20, np.mean(success_list_20),
np.mean(reachtime_list_20)))
print(
"success threshold: {} | success ratio: {:.2f} | Average reach time: {:.2f}"
.format(success_threshold_10, np.mean(success_list_10),
np.mean(reachtime_list_10)))
print(
"success threshold: {} | success ratio: {:.2f} | Average reach time: {:.2f}"
.format(success_threshold_5, np.mean(success_list_5),
np.mean(reachtime_list_5)))
if args.verbose > 0 and len(episode_lengths) > 0:
print("Mean episode length: {:.2f} +/- {:.2f}".format(
np.mean(episode_lengths), np.std(episode_lengths)))
# added by Pierre
print("path:", log_path)
d = {
"Eval mean reward": np.mean(episode_rewards),
"Eval std": np.std(episode_rewards),
"success ratio 50mm": np.mean(success_list_50),
"Average reach time 50mm": np.mean(reachtime_list_50),
"success ratio 20mm": np.mean(success_list_20),
"Average reach time 20mm": np.mean(reachtime_list_20),
"success ratio 10mm": np.mean(success_list_10),
"Average reach time 10mm": np.mean(reachtime_list_10),
"success ratio 5mm": np.mean(success_list_5),
"Average reach time 5mm": np.mean(reachtime_list_5),
}
df = pd.DataFrame(d, index=[0])
if args.random_pol:
log_rand = args.log_dir_random
df.to_csv(log_rand + "/stats.csv", index=False)
else:
df.to_csv(log_path + "/stats.csv", index=False)
# Workaround for https://github.com/openai/gym/issues/893
if not args.no_render:
if args.n_envs == 1 and 'Bullet' not in env_id and not is_atari and isinstance(
env, VecEnv):
# DummyVecEnv
# Unwrap env
while isinstance(env, VecNormalize) or isinstance(
env, VecFrameStack):
env = env.venv
env.envs[0].env.close()
else:
# SubprocVecEnv
env.close()
def rollout_halfspaces(env_id='CartPole-v1',algo='dqn',num_samples=20, precision=0.0001, render=False):
seed = 0
folder = 'rl-baselines-zoo/trained_agents'
n_envs = 1
no_render = False
deterministic = True
stochastic = False
norm_reward=False
log_path = os.path.join(folder, algo)
assert os.path.isdir(log_path), "The {} folder was not found".format(log_path)
model_path = find_saved_model(algo, log_path, env_id, load_best=False)
set_global_seeds(seed)
is_atari = 'NoFrameskip' in env_id
stats_path = os.path.join(log_path, env_id)
hyperparams, stats_path = get_saved_hyperparams(stats_path, norm_reward=norm_reward, test_mode=True)
log_dir = None
env_kwargs = {}
env = create_test_env(env_id, n_envs=n_envs, is_atari=is_atari,
stats_path=stats_path, seed=seed, log_dir=log_dir,
should_render=not no_render,
hyperparams=hyperparams, env_kwargs=env_kwargs)
# ACER raises errors because the environment passed must have
# the same number of environments as the model was trained on.
load_env = None if algo == 'acer' else env
model = ALGOS[algo].load(model_path, env=load_env)
env = gym.make('CartPole-v1')
obs = env.reset()
# Force deterministic for DQN, DDPG, SAC and HER (that is a wrapper around)
deterministic = deterministic or algo in ['dqn', 'ddpg', 'sac', 'her', 'td3'] and not stochastic
episode_reward = 0.0
episode_rewards, episode_lengths = [], []
ep_len = 0
# For HER, monitor success rate
successes = []
state = None
embedder = indicator_feature
halfspaces = {}
for i in range(num_samples):
print("+"*10)
#sample random state to start in
#TODO: maybe reset with random actions? How to make it realistic? Does it matter. Let's just try random for now to test weird edge cases.
obs = env.reset(uniform=True) #sample more uniformly than typical
start_state = obs.copy()
print("start state", obs)
#find out the "near optimal" action for this state to compare other actions to
opt_action, _ = model.predict(obs, state=state, deterministic=deterministic)
#take this action
print("TEACHER ACTION", opt_action)
obs = env.reset(start_state=start_state)
print("init state", obs)
if render:
env.render()
# input()
ep_ret = 0
fcounts = embedder(start_state)
#do initial action
obs, r, done, info = env.step(opt_action) # take a random action
fcounts += embedder(obs) #TODO: discount??
ep_ret += r
#print(r, obs)
if done:
#sample again, since started with terminal state
continue
#run tester policy thereafter
while True:
#env.render()
#TODO: sample within allowable range of angle and position
action, state = model.predict(obs, state=state, deterministic=deterministic)
# Random Agent
# action = [env.action_space.sample()]
# Clip Action to avoid out of bound errors
if isinstance(env.action_space, gym.spaces.Box):
action = np.clip(action, env.action_space.low, env.action_space.high)
#a = env.action_space.sample()
#print(obs, action)
obs, r, done, info = env.step(action) # take a random action
fcounts += embedder(obs)
#print(obs)
#print(done)
ep_ret += r
#print(r, obs)
if done:
print("final state", obs)
print("return", ep_ret)
print("fcounts", fcounts)
opt_fcounts = fcounts
break
# input()
#obs = env.reset_state(env.observation_space.sample())
#rollout once for each action and compute feature counts
fcount_vectors = []
init_actions = []
##rollout code:
for init_action in range(env.action_space.n):
if init_action == opt_action:
#don't need to roll this out since we already did
continue
print("ACTION", init_action)
obs = env.reset(start_state=start_state)
print("init state", obs)
if render:
env.render()
# input()
ep_ret = 0
fcounts = embedder(start_state)
#do initial action
obs, r, done, info = env.step(init_action) # take a random action
fcounts += embedder(obs) #TODO: discount??
ep_ret += r
#print(r, obs)
if done:
print("final state", obs)
print("return", ep_ret)
print("fcounts", fcounts)
fcount_vectors.append(fcounts)
init_actions.append(init_action)
continue
#run tester policy thereafter
while True:
#env.render()
#TODO: sample within allowable range of angle and position
action, state = model.predict(obs, state=state, deterministic=deterministic)
# Random Agent
# action = [env.action_space.sample()]
# Clip Action to avoid out of bound errors
if isinstance(env.action_space, gym.spaces.Box):
action = np.clip(action, env.action_space.low, env.action_space.high)
#a = env.action_space.sample()
#print(obs, action)
obs, r, done, info = env.step(action) # take a random action
fcounts += embedder(obs)
#print(obs)
#print(done)
ep_ret += r
#print(r, obs)
if done:
print("final state", obs)
print("return", ep_ret)
print("fcounts", fcounts)
break
normal_vector = opt_fcounts - fcounts
print("action {} over {} => fcount diff = {}".format(opt_fcounts, init_action, normal_vector))
if np.linalg.norm(normal_vector) > precision:
halfspaces[tuple(start_state), init_action, opt_action] = normal_vector
input()
#TODO: put this inside one of the value alignment verification classes to get sa_fcount_diffs and hopefully reuse that code
#then visualize test cases
# input()
# for _ in range(args.n_timesteps):
# action, state = model.predict(obs, state=state, deterministic=deterministic)
# # Random Agent
# # action = [env.action_space.sample()]
# # Clip Action to avoid out of bound errors
# if isinstance(env.action_space, gym.spaces.Box):
# action = np.clip(action, env.action_space.low, env.action_space.high)
# obs, reward, done, infos = env.step(action)
# if not args.no_render:
# env.render('human')
# episode_reward += reward
# ep_len += 1
# if args.n_envs == 1:
# # For atari the return reward is not the atari score
# # so we have to get it from the infos dict
# if is_atari and infos is not None and args.verbose >= 1:
# episode_infos = infos.get('episode')
# if episode_infos is not None:
# print("Atari Episode Score: {:.2f}".format(episode_infos['r']))
# print("Atari Episode Length", episode_infos['l'])
# if done and not is_atari and args.verbose > 0:
# # NOTE: for env using VecNormalize, the mean reward
# # is a normalized reward when `--norm_reward` flag is passed
# print("Episode Reward: {:.2f}".format(episode_reward))
# print("Episode Length", ep_len)
# state = None
# episode_rewards.append(episode_reward)
# episode_lengths.append(ep_len)
# episode_reward = 0.0
# ep_len = 0
# # Reset also when the goal is achieved when using HER
# if done or infos.get('is_success', False):
# if args.algo == 'her' and args.verbose > 1:
# print("Success?", infos[0].get('is_success', False))
# # Alternatively, you can add a check to wait for the end of the episode
# # if done:
# obs = env.reset()
# if args.algo == 'her':
# successes.append(infos[0].get('is_success', False))
# episode_reward, ep_len = 0.0, 0
# if args.verbose > 0 and len(successes) > 0:
# print("Success rate: {:.2f}%".format(100 * np.mean(successes)))
# if args.verbose > 0 and len(episode_rewards) > 0:
# print("Mean reward: {:.2f} +/- {:.2f}".format(np.mean(episode_rewards), np.std(episode_rewards)))
# if args.verbose > 0 and len(episode_lengths) > 0:
# print("Mean episode length: {:.2f} +/- {:.2f}".format(np.mean(episode_lengths), np.std(episode_lengths)))
# Workaround for https://github.com/openai/gym/issues/893
if not no_render:
if n_envs == 1 and 'Bullet' not in env_id and not is_atari and isinstance(env, VecEnv):
# DummyVecEnv
# Unwrap env
while isinstance(env, VecNormalize) or isinstance(env, VecFrameStack):
env = env.venv
env.envs[0].env.close()
else:
# SubprocVecEnv
env.close()
return halfspaces
f, _ = plot_results(results, average_group=False, shaded_std=False)
f.savefig(os.path.join(save_path, 'results.png'),
bbox_inches='tight',
format='png')
if args.play > 0:
test_path = os.path.join(save_path, 'test')
env_hyperparams = {
'normalize': normalize,
'n_stack': n_stack,
'normalize_kwargs': normalize_kwargs
}
env = create_test_env(env_id,
n_envs=1,
stats_path=params_path,
log_dir=test_path,
hyperparams=env_hyperparams,
env_params=env_params)
env.reset()
env = VecVideoRecorder(env,
test_path,
record_video_trigger=lambda x: x == 0,
video_length=args.play,
name_prefix="{}-{}-{}-final".format(
exp_name, args.algo, env_id))
obs = env.reset()
for _ in range(args.play + 1):
# action = [env.action_space.sample()]
action, _ = model.predict(obs, deterministic=True)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env', help='environment ID', type=str, default='CartPole-v1')
parser.add_argument('-f', '--folder', help='Log folder', type=str, default='trained_agents')
parser.add_argument('--algo', help='RL Algorithm', default='ppo2',
type=str, required=False, choices=list(ALGOS.keys()))
parser.add_argument('-n', '--n-timesteps', help='number of timesteps', default=1000,
type=int)
parser.add_argument('--n-envs', help='number of environments', default=1,
type=int)
parser.add_argument('--exp-id', help='Experiment ID (default: -1, no exp folder, 0: latest)', default=-1,
type=int)
parser.add_argument('--verbose', help='Verbose mode (0: no output, 1: INFO)', default=1,
type=int)
parser.add_argument('--no-render', action='store_true', default=False,
help='Do not render the environment (useful for tests)')
parser.add_argument('--deterministic', action='store_true', default=False,
help='Use deterministic actions')
parser.add_argument('--stochastic', action='store_true', default=False,
help='Use stochastic actions (for DDPG/DQN/SAC)')
parser.add_argument('--load-best', action='store_true', default=False,
help='Load best model instead of last model if available')
parser.add_argument('--norm-reward', action='store_true', default=False,
help='Normalize reward if applicable (trained with VecNormalize)')
parser.add_argument('--seed', help='Random generator seed', type=int, default=0)
parser.add_argument('--reward-log', help='Where to log reward', default='', type=str)
parser.add_argument('--gym-packages', type=str, nargs='+', default=[], help='Additional external Gym environemnt package modules to import (e.g. gym_minigrid)')
parser.add_argument('--render-pybullet', help='Slow down Pybullet simulation to render', default=False) # added by Pierre
parser.add_argument('--random-pol', help='Random policy', default=False) # added by Pierre
args = parser.parse_args()
plot_bool = True
plot_dim = 2
log_bool = False
if plot_bool:
if plot_dim == 2:
fig, (ax1, ax2) = plt.subplots(2, 1, sharey=True, figsize=(5, 10))
elif plot_dim == 3:
fig = plt.figure()
ax = fig.gca(projection='3d')
if log_bool:
output_df = pd.DataFrame()
# Going through custom gym packages to let them register in the global registory
for env_module in args.gym_packages:
importlib.import_module(env_module)
env_id = args.env
algo = args.algo
folder = args.folder
if args.exp_id == 0:
args.exp_id = get_latest_run_id(os.path.join(folder, algo), env_id)
print('Loading latest experiment, id={}'.format(args.exp_id))
# Sanity checks
if args.exp_id > 0:
log_path = os.path.join(folder, algo, '{}_{}'.format(env_id, args.exp_id))
else:
log_path = os.path.join(folder, algo)
assert os.path.isdir(log_path), "The {} folder was not found".format(log_path)
if not args.random_pol: # added by Pierre
model_path = find_saved_model(algo, log_path, env_id, load_best=args.load_best)
if algo in ['dqn', 'ddpg', 'sac', 'td3']:
args.n_envs = 1
set_global_seeds(args.seed)
is_atari = 'NoFrameskip' in env_id
stats_path = os.path.join(log_path, env_id)
hyperparams, stats_path = get_saved_hyperparams(stats_path, norm_reward=args.norm_reward, test_mode=True)
log_dir = args.reward_log if args.reward_log != '' else None
env = create_test_env(env_id, n_envs=args.n_envs, is_atari=is_atari,
stats_path=stats_path, seed=args.seed, log_dir=log_dir,
should_render=not args.no_render,
hyperparams=hyperparams)
# ACER raises errors because the environment passed must have
# the same number of environments as the model was trained on.
load_env = None if algo == 'acer' else env
if not args.random_pol: # added by Pierre
model = ALGOS[algo].load(model_path, env=load_env)
# if not args.no_render:
# env.render(mode="human") # added by Pierre (to work with ReachingJaco-v1)
obs = env.reset()
# Force deterministic for DQN, DDPG, SAC and HER (that is a wrapper around)
deterministic = args.deterministic or algo in ['dqn', 'ddpg', 'sac', 'her', 'td3'] and not args.stochastic
episode_reward = 0.0
episode_rewards, episode_lengths = [], []
ep_len = 0
episode = 0
# success_threshold_001 = 0.01
# success_list_001, reachtime_list_001, episode_success_list_001 = [], [], []
# success_threshold_0002 = 0.002
# success_list_0002, reachtime_list_0002, episode_success_list_0002 = [], [], []
# success_threshold_0001 = 0.001
# success_list_0001, reachtime_list_0001, episode_success_list_0001 = [], [], []
# success_threshold_00005 = 0.0005
# success_list_00005, reachtime_list_00005, episode_success_list_00005 = [], [], []
# changed for the paper
success_threshold_50 = 0.05
success_list_50, reachtime_list_50, episode_success_list_50 = [], [], []
success_threshold_20 = 0.02
success_list_20, reachtime_list_20, episode_success_list_20 = [], [], []
success_threshold_10 = 0.01
success_list_10, reachtime_list_10, episode_success_list_10 = [], [], []
success_threshold_5 = 0.005
success_list_5, reachtime_list_5, episode_success_list_5 = [], [], []
# For HER, monitor success rate
successes = []
state = None
for _ in range(args.n_timesteps):
if args.random_pol:
# Random Agent
action = [env.action_space.sample()]
else:
action, state = model.predict(obs, state=state, deterministic=deterministic)
# Clip Action to avoid out of bound errors
if isinstance(env.action_space, gym.spaces.Box):
action = np.clip(action, env.action_space.low, env.action_space.high)
obs, reward, done, infos = env.step(action)
if args.render_pybullet:
time.sleep(1./30.) # added by Pierre (slow down Pybullet for rendering)
if infos[0]['total_distance'] <= success_threshold_50:
episode_success_list_50.append(1)
else:
episode_success_list_50.append(0)
if infos[0]['total_distance'] <= success_threshold_20:
episode_success_list_20.append(1)
else:
episode_success_list_20.append(0)
if infos[0]['total_distance'] <= success_threshold_10:
episode_success_list_10.append(1)
else:
episode_success_list_10.append(0)
if infos[0]['total_distance'] <= success_threshold_5:
episode_success_list_5.append(1)
else:
episode_success_list_5.append(0)
if plot_bool:
goal = infos[0]['goal position']
tip = infos[0]['tip position']
if plot_dim == 2:
ax1.cla()
ax1.plot(goal[0], goal[2], marker='x', color='b', linestyle='', markersize=10, label="goal", mew=3)
ax1.plot(tip[0], tip[2], marker='o', color='r', linestyle='', markersize=10, label="end effector")
circ_1_50 = plt.Circle((goal[0], goal[2]), radius=success_threshold_50, edgecolor='g', facecolor='w', linestyle='--', label="50 mm")
circ_1_20 = plt.Circle((goal[0], goal[2]), radius=success_threshold_20, edgecolor='b', facecolor='w', linestyle='--', label="20 mm")
circ_1_10 = plt.Circle((goal[0], goal[2]), radius=success_threshold_10, edgecolor='m', facecolor='w', linestyle='--', label="10 mm")
circ_1_5 = plt.Circle((goal[0], goal[2]), radius=success_threshold_5, edgecolor='r', facecolor='w', linestyle='--', label="5 mm")
ax1.add_patch(circ_1_50)
ax1.add_patch(circ_1_20)
ax1.add_patch(circ_1_10)
ax1.add_patch(circ_1_5)
ax1.set_xlim([-0.25, 0.25])
ax1.set_ylim([0, 0.5])
ax1.set_xlabel("x (m)", fontsize=15)
ax1.set_ylabel("z (m)", fontsize=15)
ax2.cla()
ax2.plot(goal[1], goal[2], marker='x', color='b', linestyle='', markersize=10, mew=3)
ax2.plot(tip[1], tip[2], marker='o', color='r', linestyle='', markersize=10)
circ_2_50 = plt.Circle((goal[1], goal[2]), radius=success_threshold_50, edgecolor='g', facecolor='w', linestyle='--')
circ_2_20 = plt.Circle((goal[1], goal[2]), radius=success_threshold_20, edgecolor='b', facecolor='w', linestyle='--')
circ_2_10 = plt.Circle((goal[1], goal[2]), radius=success_threshold_10, edgecolor='m', facecolor='w', linestyle='--')
circ_2_5 = plt.Circle((goal[1], goal[2]), radius=success_threshold_5, edgecolor='r', facecolor='w', linestyle='--')
ax2.add_patch(circ_2_50)
ax2.add_patch(circ_2_20)
ax2.add_patch(circ_2_10)
ax2.add_patch(circ_2_5)
ax2.set_xlim([-0.25, 0.25])
ax2.set_ylim([0, 0.5])
ax2.set_xlabel("y (m)", fontsize=15)
ax2.set_ylabel("z (m)", fontsize=15)
ax1.legend(loc='upper left', bbox_to_anchor=(0, 1.2), ncol=3, fancybox=True, shadow=True)
elif plot_dim == 3:
ax.cla()
ax.plot([tip[0]], [tip[1]], zs=[tip[2]], marker='x', color='b')
ax.plot([goal[0]], [goal[1]], zs=[goal[2]], marker='o', color='r', linestyle="None")
ax.set_xlim([-0.2, 0.2])
ax.set_ylim([-0.2, 0.2])
ax.set_zlim([0, 0.5])
ax.set_xlabel("x (m)", fontsize=15)
ax.set_ylabel("y (m)", fontsize=15)
ax.set_zlabel("z (m)", fontsize=15)
fig.suptitle("timestep "+str(ep_len)+" | distance to target: "+str(round(infos[0]['total_distance']*1000, 1))+" mm")
plt.pause(0.01)
# plt.show()
if log_bool:
dict_log = infos[0]
dict_log['action'] = action[0]
dict_log['obs'] = obs[0]
dict_log['reward'] = reward[0]
dict_log['done'] = done[0]
dict_log['timestep'] = ep_len
dict_log['episode'] = episode
output_df = output_df.append(dict_log, ignore_index=True)
# if not args.no_render:
# env.render('human')
episode_reward += reward[0]
ep_len += 1
if args.n_envs == 1:
# For atari the return reward is not the atari score
# so we have to get it from the infos dict
if is_atari and infos is not None and args.verbose >= 1:
episode_infos = infos[0].get('episode')
if episode_infos is not None:
print("Atari Episode Score: {:.2f}".format(episode_infos['r']))
print("Atari Episode Length", episode_infos['l'])
if done and not is_atari and args.verbose > 0:
# NOTE: for env using VecNormalize, the mean reward
# is a normalized reward when `--norm_reward` flag is passed
print("Episode nb: {} | Episode Reward: {:.2f} | Episode Length: {}".format(episode, episode_reward, ep_len))
# print("Episode Length", ep_len) # commented by Pierre
state = None
episode_rewards.append(episode_reward)
episode_lengths.append(ep_len)
# append the last element of the episode success list when episode is done
success_list_50.append(episode_success_list_50[-1])
success_list_20.append(episode_success_list_20[-1])
success_list_10.append(episode_success_list_10[-1])
success_list_5.append(episode_success_list_5[-1])
# if the episode is successful and it starts from an unsucessful step, calculate reach time
if episode_success_list_50[-1] == True and episode_success_list_50[0] == False:
idx = 0
while episode_success_list_50[idx] == False:
idx += 1
reachtime_list_50.append(idx)
if episode_success_list_20[-1] == True and episode_success_list_20[0] == False:
idx = 0
while episode_success_list_20[idx] == False:
idx += 1
reachtime_list_20.append(idx)
if episode_success_list_10[-1] == True and episode_success_list_10[0] == False:
idx = 0
while episode_success_list_10[idx] == False:
idx += 1
reachtime_list_10.append(idx)
if episode_success_list_5[-1] == True and episode_success_list_5[0] == False:
idx = 0
while episode_success_list_5[idx] == False:
idx += 1
reachtime_list_5.append(idx)
if log_bool:
# output_df.to_csv(log_path+"/res_episode_"+str(episode)+".csv", index=False) # slow
output_df.to_pickle(log_path+"/res_episode_"+str(episode)+".pkl")
# reset for new episode
episode_reward = 0.0
ep_len = 0
episode_success_list_50 = []
episode_success_list_20 = []
episode_success_list_10 = []
episode_success_list_5 = []
episode += 1
# Reset also when the goal is achieved when using HER
if done or infos[0].get('is_success', False):
if args.algo == 'her' and args.verbose > 1:
print("Success?", infos[0].get('is_success', False))
# Alternatively, you can add a check to wait for the end of the episode
# if done:
obs = env.reset()
if args.algo == 'her':
successes.append(infos[0].get('is_success', False))
episode_reward, ep_len = 0.0, 0
if args.verbose > 0 and len(successes) > 0:
print("Success rate: {:.2f}%".format(100 * np.mean(successes)))
if args.verbose > 0 and len(episode_rewards) > 0:
print("Mean reward: {:.2f} +/- {:.2f}".format(np.mean(episode_rewards), np.std(episode_rewards)))
print("success threshold: {} | success ratio: {:.2f} | Average reach time: {:.2f}".format(success_threshold_50, np.mean(success_list_50), np.mean(reachtime_list_50)))
print("success threshold: {} | success ratio: {:.2f} | Average reach time: {:.2f}".format(success_threshold_20, np.mean(success_list_20), np.mean(reachtime_list_20)))
print("success threshold: {} | success ratio: {:.2f} | Average reach time: {:.2f}".format(success_threshold_10, np.mean(success_list_10), np.mean(reachtime_list_10)))
print("success threshold: {} | success ratio: {:.2f} | Average reach time: {:.2f}".format(success_threshold_5, np.mean(success_list_5), np.mean(reachtime_list_5)))
# added by Pierre
print("path:", log_path)
d = {
"Eval mean reward": np.mean(episode_rewards),
"Eval std": np.std(episode_rewards),
"success ratio 50mm": np.mean(success_list_50),
"Average reach time 50mm": np.mean(reachtime_list_50),
"success ratio 20mm": np.mean(success_list_20),
"Average reach time 20mm": np.mean(reachtime_list_20),
"success ratio 10mm": np.mean(success_list_10),
"Average reach time 10mm": np.mean(reachtime_list_10),
"success ratio 5mm": np.mean(success_list_5),
"Average reach time 5mm": np.mean(reachtime_list_5),
}
df = pd.DataFrame(d, index=[0])
if args.random_pol:
df.to_csv("logs/random_policy_0.2M/"+env_id+"/stats.csv", index=False) # make path naming more robust
else:
df.to_csv(log_path+"/stats.csv", index=False)
if args.verbose > 0 and len(episode_lengths) > 0:
print("Mean episode length: {:.2f} +/- {:.2f}".format(np.mean(episode_lengths), np.std(episode_lengths)))
# Workaround for https://github.com/openai/gym/issues/893
if not args.no_render:
if args.n_envs == 1 and 'Bullet' not in env_id and not is_atari and isinstance(env, VecEnv):
# DummyVecEnv
# Unwrap env
while isinstance(env, VecNormalize) or isinstance(env, VecFrameStack):
env = env.venv
env.envs[0].env.close()
else:
# SubprocVecEnv
env.close()
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]
model_path = os.path.join(train_graph_dir, args.env + ".zip")
print("Loading model from", model_path)
log_dir = "/tmp"
hyperparams, stats_path = get_saved_hyperparams(model_path,
norm_reward=False,
test_mode=True)
print("Running ", args.algo, " on ", args.env)
set_global_seeds(0)
env = create_test_env(args.env,
n_envs=1,
is_atari=is_atari,
stats_path=stats_path,
seed=0,
log_dir=log_dir,
should_render=False,
hyperparams=hyperparams)
print("Evaluating converted model")
episode_rewards, lengths, norm_rewards = [], [], []
for i in range(100):
obs, done = env.reset(), False
episode_rew, norm_rew, length = 0, 0.0, 0
while not done:
interpreter.set_tensor(input_index, obs)
interpreter.invoke()
predictions = interpreter.get_tensor(output_index)
action = predictions
if isinstance(env.action_space,
log_path = os.path.join(folder, algo)
model_path = os.path.join(log_path, f"{env_id}.zip")
#model_path = os.path.join(log_path, "best_model.zip")
stats_path = os.path.join(log_path, env_id)
hyperparams, stats_path = get_saved_hyperparams(stats_path)
is_atari = "NoFrameskip" in env_id
env = create_test_env(
env_id,
n_envs=n_envs,
stats_path=stats_path,
seed=seed,
log_dir=None,
should_render=not args.no_render,
hyperparams=hyperparams,
)
model = ALGOS[algo].load(model_path)
obs = env.reset()
# Note: apparently it renders by default
env = VecVideoRecorder(
env,
video_folder,
record_video_trigger=lambda x: x == 0,
video_length=video_length,
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env',
help='environment ID',
type=str,
default='CartPole-v1')
parser.add_argument('-f',
'--folder',
help='Log folder',
type=str,
default='trained_agents')
parser.add_argument('--algo',
help='RL Algorithm',
default='ppo2',
type=str,
required=False,
choices=list(ALGOS.keys()))
# parser.add_argument('-n', '--n-timesteps', help='number of timesteps', default=1000,
# type=int)
parser.add_argument('-n',
'--n-episodes',
help='number of episodes to collect',
default=20,
type=int)
parser.add_argument('--n-envs',
help='number of environments',
default=1,
type=int)
parser.add_argument(
'--exp-id',
help='Experiment ID (default: -1, no exp folder, 0: latest)',
default=-1,
type=int)
parser.add_argument('--verbose',
help='Verbose mode (0: no output, 1: INFO)',
default=1,
type=int)
parser.add_argument(
'--no-render',
action='store_true',
default=False,
help='Do not render the environment (useful for tests)')
# for deterministic (bool type)
parser.add_argument('--deterministic',
dest='deterministic',
action='store_true')
parser.add_argument('--no-deterministic',
dest='deterministic',
action='store_false')
parser.set_defaults(deterministic=True) # true by default
# parser.add_argument('--deterministic', action='store_true', default=False,
# help='Use deterministic actions')
# parser.add_argument('--stochastic', action='store_true', default=False,
# help='Use stochastic actions (for DDPG/DQN/SAC)')
parser.add_argument(
'--norm-reward',
action='store_true',
default=False,
help='Normalize reward if applicable (trained with VecNormalize)')
parser.add_argument('--seed',
help='Random generator seed',
type=int,
default=0)
parser.add_argument('--reward-log',
help='Where to log reward',
default='',
type=str)
parser.add_argument(
'--gym-packages',
type=str,
nargs='+',
default=[],
help=
'Additional external Gym environemnt package modules to import (e.g. gym_minigrid)'
)
args = parser.parse_args()
# Going through custom gym packages to let them register in the global registory
for env_module in args.gym_packages:
importlib.import_module(env_module)
env_id = args.env
algo = args.algo
folder = args.folder
if args.exp_id == 0:
args.exp_id = get_latest_run_id(os.path.join(folder, algo), env_id)
print('Loading latest experiment, id={}'.format(args.exp_id))
# Sanity checks
if args.exp_id > 0:
log_path = os.path.join(folder, algo,
'{}_{}'.format(env_id, args.exp_id))
else:
log_path = os.path.join(folder, algo)
assert os.path.isdir(log_path), "The {} folder was not found".format(
log_path)
stats_path = os.path.join(log_path, env_id)
hyperparams, stats_path = get_saved_hyperparams(
stats_path, norm_reward=args.norm_reward, test_mode=True)
log_dir = args.reward_log if args.reward_log != '' else None
if algo in ['dqn', 'ddpg', 'sac', 'td3']:
args.n_envs = 1
set_global_seeds(args.seed)
is_atari = 'NoFrameskip' in env_id
env = create_test_env(env_id,
n_envs=args.n_envs,
is_atari=is_atari,
stats_path=stats_path,
seed=args.seed,
log_dir=log_dir,
should_render=not args.no_render,
hyperparams=hyperparams)
model_path = find_saved_model(algo, log_path, env_id)
model = ALGOS[algo].load(model_path, env=env)
# Force deterministic for DQN, DDPG, SAC and HER (that is a wrapper around)
# deterministic = args.deterministic or algo in ['dqn', 'ddpg', 'sac', 'her', 'td3'] and not args.stochastic
deterministic = args.deterministic
save_dir = os.path.join("expert_trajs_by_info_deterministic_with_std",
algo)
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
runner(env,
env_id,
model,
args.n_episodes,
deterministic,
save=True,
save_dir=save_dir)
if algo in ['dqn', 'ddpg']:
args.n_envs = 1
set_global_seeds(args.seed)
is_atari = 'NoFrameskip' in env_id
stats_path = "{}/{}/{}/".format(folder, algo, env_id)
if not os.path.isdir(stats_path):
stats_path = None
using_vec_normalize = stats_path is not None
log_dir = args.reward_log if args.reward_log != '' else None
env = create_test_env(env_id, n_envs=args.n_envs, is_atari=is_atari,
stats_path=stats_path, norm_reward=args.norm_reward,
seed=args.seed, log_dir=log_dir, should_render=not args.no_render)
model = ALGOS[algo].load(model_path)
obs = env.reset()
# Force deterministic for DQN and DDPG
deterministic = args.deterministic or algo in ['dqn', 'ddpg']
running_reward = 0.0
ep_len = 0
for _ in range(args.n_timesteps):
action, _ = model.predict(obs, deterministic=deterministic)
# Random Agent
# action = [env.action_space.sample()]
def main(): # noqa: C901
parser = argparse.ArgumentParser()
parser.add_argument("--env",
help="environment ID",
type=str,
default="Walker2DBulletEnv-v0")
parser.add_argument("--algo",
help="RL Algorithm",
default="ppo",
type=str,
required=False,
choices=list(ALGOS.keys()))
parser.add_argument("-n",
"--n-timesteps",
help="number of timesteps",
default=1000,
type=int)
parser.add_argument(
"--num-threads",
help="Number of threads for PyTorch (-1 to use default)",
default=-1,
type=int)
parser.add_argument("--n-envs",
help="number of environments",
default=1,
type=int)
parser.add_argument(
"--exp-id",
help="Experiment ID (default: 0: latest, -1: no exp folder)",
default=0,
type=int)
parser.add_argument("--verbose",
help="Verbose mode (0: no output, 1: INFO)",
default=1,
type=int)
parser.add_argument(
"--no-render",
action="store_true",
default=False,
help="Do not render the environment (useful for tests)")
parser.add_argument("--deterministic",
action="store_true",
default=True,
help="Use deterministic actions")
parser.add_argument(
"--load-best",
action="store_true",
default=False,
help="Load best model instead of last model if available")
parser.add_argument("--stochastic",
action="store_true",
default=False,
help="Use stochastic actions (for DDPG/DQN/SAC)")
parser.add_argument(
"--norm-reward",
action="store_true",
default=False,
help="Normalize reward if applicable (trained with VecNormalize)")
parser.add_argument("--seed",
help="Random generator seed",
type=int,
default=0)
parser.add_argument("--reward-log",
help="Where to log reward",
default="",
type=str)
parser.add_argument(
"--gym-packages",
type=str,
nargs="+",
default=[],
help=
"Additional external Gym environemnt package modules to import (e.g. gym_minigrid)",
)
parser.add_argument(
"--env-kwargs",
type=str,
nargs="+",
action=StoreDict,
help="Optional keyword argument to pass to the env constructor")
# === #
parser.add_argument("--load-checkpoint",
type=str,
help="pass the path of zip file corresponding to it")
parser.add_argument("-f",
"--folder",
help="Log folder",
type=str,
default="rl-trained-agents")
parser.add_argument("--dataset", type=str, default="dataset/walker2d_v6")
parser.add_argument("--body-id", type=int, default=0)
args = parser.parse_args()
dataset_name, env_id, train_files, train_params, train_names, test_files, test_params, test_names = load_dataset.load_dataset(
args.dataset, seed=0, shuffle=False, train_proportion=1)
# Going through custom gym packages to let them register in the global registory
for env_module in args.gym_packages:
importlib.import_module(env_module)
# env_id = args.env
algo = args.algo
log_path = args.folder
# if args.exp_id == 0:
# args.exp_id = get_latest_run_id(os.path.join(folder, algo), env_id)
# print(f"Loading latest experiment, id={args.exp_id}")
# # Sanity checks
# if args.exp_id > 0:
# log_path = os.path.join(folder, algo, f"{env_id}_{args.exp_id}")
# else:
# log_path = os.path.join(folder, algo)
# assert os.path.isdir(log_path), f"The {log_path} folder was not found"
# found = False
# for ext in ["zip"]:
# model_path = os.path.join(log_path, f"{env_id}.{ext}")
# found = os.path.isfile(model_path)
# if found:
# break
# if args.load_best:
# model_path = os.path.join(log_path, "best_model.zip")
# found = os.path.isfile(model_path)
# if args.load_checkpoint is not None:
# model_path = os.path.join(log_path, f"rl_model_{args.load_checkpoint}_steps.zip")
# found = os.path.isfile(model_path)
# if not found:
# raise ValueError(f"No model found for {algo} on {env_id}, path: {model_path}")
model_path = args.load_checkpoint
if algo in ["dqn", "ddpg", "sac", "td3", "tqc"]:
args.n_envs = 1
set_random_seed(args.seed)
if args.num_threads > 0:
if args.verbose > 1:
print(f"Setting torch.num_threads to {args.num_threads}")
th.set_num_threads(args.num_threads)
is_atari = "NoFrameskip" in env_id
stats_path = os.path.join(log_path, env_id)
hyperparams, stats_path = get_saved_hyperparams(
stats_path, norm_reward=args.norm_reward, test_mode=True)
# load env_kwargs if existing
# env_kwargs = {}
# args_path = os.path.join(log_path, env_id, "args.yml")
# if os.path.isfile(args_path):
# with open(args_path, "r") as f:
# loaded_args = yaml.load(f, Loader=yaml.UnsafeLoader) # pytype: disable=module-attr
# if loaded_args["env_kwargs"] is not None:
# env_kwargs = loaded_args["env_kwargs"]
# # overwrite with command line arguments
# if args.env_kwargs is not None:
# env_kwargs.update(args.env_kwargs)
args.watch_eval = True
env_kwargs = {
"xml": train_files[args.body_id],
"param": train_params[args.body_id],
"render": args.watch_eval,
}
log_dir = args.reward_log if args.reward_log != "" else None
env = create_test_env(
env_id,
n_envs=args.n_envs,
stats_path=stats_path,
seed=args.seed,
log_dir=log_dir,
should_render=not args.no_render,
hyperparams=hyperparams,
env_kwargs=env_kwargs,
)
kwargs = dict(seed=args.seed)
if algo in ["dqn", "ddpg", "sac", "her", "td3", "tqc"]:
# Dummy buffer size as we don't need memory to enjoy the trained agent
kwargs.update(dict(buffer_size=1))
model = ALGOS[algo].load(model_path, env=env, **kwargs)
obs = env.reset()
# Force deterministic for DQN, DDPG, SAC and HER (that is a wrapper around)
deterministic = args.deterministic or algo in [
"dqn", "ddpg", "sac", "her", "td3", "tqc"
] and not args.stochastic
state = None
episode_reward = 0.0
episode_rewards, episode_lengths = [], []
ep_len = 0
# For HER, monitor success rate
successes = []
for _ in range(args.n_timesteps):
action, state = model.predict(obs,
state=state,
deterministic=deterministic)
# Random Agent
# action = [env.action_space.sample()]
# Clip Action to avoid out of bound errors
if isinstance(env.action_space, gym.spaces.Box):
action = np.clip(action, env.action_space.low,
env.action_space.high)
obs, reward, done, infos = env.step(action)
sleep(0.01)
if not args.no_render:
env.render("human")
episode_reward += reward[0]
ep_len += 1
if args.n_envs == 1:
# For atari the return reward is not the atari score
# so we have to get it from the infos dict
if is_atari and infos is not None and args.verbose >= 1:
episode_infos = infos[0].get("episode")
if episode_infos is not None:
print(f"Atari Episode Score: {episode_infos['r']:.2f}")
print("Atari Episode Length", episode_infos["l"])
if done and not is_atari and args.verbose > 0:
# NOTE: for env using VecNormalize, the mean reward
# is a normalized reward when `--norm_reward` flag is passed
print(f"Episode Reward: {episode_reward:.2f}")
print("Episode Length", ep_len)
episode_rewards.append(episode_reward)
episode_lengths.append(ep_len)
episode_reward = 0.0
ep_len = 0
state = None
# Reset also when the goal is achieved when using HER
if done and infos[0].get("is_success") is not None:
if args.verbose > 1:
print("Success?", infos[0].get("is_success", False))
# Alternatively, you can add a check to wait for the end of the episode
if done:
obs = env.reset()
if infos[0].get("is_success") is not None:
successes.append(infos[0].get("is_success", False))
episode_reward, ep_len = 0.0, 0
if args.verbose > 0 and len(successes) > 0:
print("Success rate: {:.2f}%".format(100 * np.mean(successes)))
if args.verbose > 0 and len(episode_rewards) > 0:
print("Mean reward: {:.2f} +/- {:.2f}".format(np.mean(episode_rewards),
np.std(episode_rewards)))
if args.verbose > 0 and len(episode_lengths) > 0:
print("Mean episode length: {:.2f} +/- {:.2f}".format(
np.mean(episode_lengths), np.std(episode_lengths)))
# Workaround for https://github.com/openai/gym/issues/893
if not args.no_render:
if args.n_envs == 1 and "Bullet" not in env_id and not is_atari and isinstance(
env, VecEnv):
# DummyVecEnv
# Unwrap env
while isinstance(env, VecEnvWrapper):
env = env.venv
if isinstance(env, DummyVecEnv):
env.envs[0].env.close()
else:
env.close()
else:
# SubprocVecEnv
env.close()
def main(): # noqa: C901
parser = argparse.ArgumentParser()
parser.add_argument("-tb",
"--tensorboard-log",
help="Tensorboard log dir",
default="",
type=str)
parser.add_argument("--env",
help="environment ID",
type=str,
default="CartPole-v1")
parser.add_argument("-f",
"--folder",
help="Log folder",
type=str,
default="rl-trained-agents")
parser.add_argument("--algo",
help="RL Algorithm",
default="ppo",
type=str,
required=False,
choices=list(ALGOS.keys()))
parser.add_argument("-n",
"--n-timesteps",
help="number of timesteps",
default=1000,
type=int)
parser.add_argument(
"--num-threads",
help="Number of threads for PyTorch (-1 to use default)",
default=-1,
type=int)
parser.add_argument("--n-envs",
help="number of environments",
default=1,
type=int)
# parser.add_argument("--exp-id", help="Experiment ID (default: 0: latest, -1: no exp folder)", default=0, type=int)
parser.add_argument(
"--exp-id",
help="Experiment ID (default: 0: latest, -1: no exp folder)",
default='0',
type=str)
parser.add_argument("--verbose",
help="Verbose mode (0: no output, 1: INFO)",
default=1,
type=int)
parser.add_argument(
"--no-render",
action="store_true",
default=False,
help="Do not render the environment (useful for tests)")
parser.add_argument(
"--render-mode",
default='step',
help="Whether to render at each step or at the end of an episode")
parser.add_argument("--deterministic",
action="store_true",
default=False,
help="Use deterministic actions")
parser.add_argument(
"--load-best",
action="store_true",
default=False,
help="Load best model instead of last model if available")
parser.add_argument(
"--load-checkpoint",
type=int,
help="Load checkpoint instead of last model if available, "
"you must pass the number of timesteps corresponding to it",
)
parser.add_argument("--stochastic",
action="store_true",
default=False,
help="Use stochastic actions")
parser.add_argument(
"--norm-reward",
action="store_true",
default=False,
help="Normalize reward if applicable (trained with VecNormalize)")
parser.add_argument("--seed",
help="Random generator seed",
type=int,
default=0)
parser.add_argument("--info-freq",
help="Frequency on which info valuers are logged",
type=int,
default=10)
parser.add_argument("--reward-log",
help="Where to log reward",
default="",
type=str)
parser.add_argument(
"--gym-packages",
type=str,
nargs="+",
default=[],
help=
"Additional external Gym environemnt package modules to import (e.g. gym_minigrid)",
)
parser.add_argument(
"--env-kwargs",
type=str,
nargs="+",
action=StoreDict,
help="Optional keyword argument to pass to the env constructor")
args = parser.parse_args()
# Going through custom gym packages to let them register in the global registory
for env_module in args.gym_packages:
importlib.import_module(env_module)
env_id = args.env
algo = args.algo
folder = args.folder
if args.exp_id == '0':
args.exp_id = get_latest_run_id(os.path.join(folder, algo), env_id)
print(f"Loading latest experiment, id={args.exp_id}")
# Sanity checks
if args.exp_id != '0' and args.exp_id != '-1':
log_path = os.path.join(folder, algo, f"{env_id}_{args.exp_id}")
else:
log_path = os.path.join(folder, algo)
assert os.path.isdir(log_path), f"The {log_path} folder was not found"
found = False
for ext in ["zip"]:
model_path = os.path.join(log_path, f"{env_id}.{ext}")
found = os.path.isfile(model_path)
if found:
break
if args.load_best:
model_path = os.path.join(log_path, "best_model.zip")
found = os.path.isfile(model_path)
if args.load_checkpoint is not None:
model_path = os.path.join(
log_path, f"rl_model_{args.load_checkpoint}_steps.zip")
found = os.path.isfile(model_path)
if not found:
raise ValueError(
f"No model found for {algo} on {env_id}, path: {model_path}")
else:
print(f"Loading model for {algo} on {env_id}, path: {model_path}")
off_policy_algos = ["qrdqn", "dqn", "ddpg", "sac", "her", "td3", "tqc"]
if algo in off_policy_algos:
args.n_envs = 1
set_random_seed(args.seed)
if args.num_threads > 0:
if args.verbose > 1:
print(f"Setting torch.num_threads to {args.num_threads}")
th.set_num_threads(args.num_threads)
is_atari = ExperimentManager.is_atari(env_id)
stats_path = os.path.join(log_path, env_id)
hyperparams, stats_path = get_saved_hyperparams(
stats_path, norm_reward=args.norm_reward, test_mode=True)
# load env_kwargs if existing
env_kwargs = {}
args_path = os.path.join(log_path, env_id, "args.yml")
if os.path.isfile(args_path):
with open(args_path, "r") as f:
loaded_args = yaml.load(f, Loader=yaml.UnsafeLoader) # pytype: disable=module-attr
if loaded_args["env_kwargs"] is not None:
env_kwargs = loaded_args["env_kwargs"]
# overwrite with command line arguments
if args.env_kwargs is not None:
env_kwargs.update(args.env_kwargs)
log_dir = args.reward_log if args.reward_log != "" else None
env = create_test_env(
env_id,
n_envs=args.n_envs,
stats_path=stats_path,
seed=args.seed,
log_dir=log_dir,
should_render=not args.no_render,
hyperparams=hyperparams,
env_kwargs=env_kwargs,
)
kwargs = dict(seed=args.seed)
if algo in off_policy_algos:
# Dummy buffer size as we don't need memory to enjoy the trained agent
kwargs.update(dict(buffer_size=1))
# Check if we are running python 3.8+
# we need to patch saved model under python 3.6/3.7 to load them
newer_python_version = sys.version_info.major == 3 and sys.version_info.minor >= 8
custom_objects = {}
if newer_python_version:
custom_objects = {
"learning_rate": 0.0,
"lr_schedule": lambda _: 0.0,
"clip_range": lambda _: 0.0,
}
model = ALGOS[algo].load(model_path,
env=env,
custom_objects=custom_objects,
**kwargs)
# tb_path = ''
# for i in range(0,100000,1):
# tb_path = os.path.join(args.tensorboard_log, env_id, algo.upper() + "_" + str(i))
# if not os.path.exists(tb_path):
# break
# print("algo=",algo, " logdir=", tb_path)
# writer = SummaryWriter(log_dir=tb_path)
obs = env.reset()
# Deterministic by default except for atari games
stochastic = args.stochastic or is_atari and not args.deterministic
deterministic = not stochastic
state = None
episode_reward = 0.0
episode_rewards, episode_lengths = [], []
ep_len = 0
ep_count = 0
# For HER, monitor success rate
successes = []
sbcommon_utils.configure_logger(args.verbose,
os.path.join(args.tensorboard_log, env_id),
algo.upper(),
reset_num_timesteps=True)
xlsx_logpath = os.path.join(
args.tensorboard_log,
env_id) if logger.get_dir() is None else logger.get_dir()
xlsx_logger = Xlsx_Logger(xlsx_logpath, env_id)
with open(os.path.join(xlsx_logpath, 'args.yaml'), 'w') as file:
yaml.dump(args, file)
fig: plt.Figure = None
info_freq = args.info_freq
try:
for step in range(args.n_timesteps):
action, state = model.predict(obs,
state=state,
deterministic=deterministic)
obs, reward, done, infos = env.step(action)
episode_reward += reward[0]
ep_len += 1
if args.n_envs == 1:
# log info variables to tensorboard
if (step % info_freq == 0 or done) and type(infos[0]) is dict:
if not args.no_render:
if not done and args.render_mode == 'step':
fig = env.render("human")
elif done and args.render_mode == 'episode':
fig = env.envs[0].rendered_episode
xlsx_logger.set_step_ep(ep_count, step)
for key in infos[0]:
if key == 'episode' or key == 'terminal_observation' or key == 'render':
continue
val = infos[0].get(key)
logger.record("eval/" + key, val, exclude='stdout')
xlsx_logger.log(key, val)
if fig is not None:
log_fig = logger.Figure(fig, False)
logger.record("eval/figure", log_fig, exclude='stdout')
# writer.add_scalar("eval/"+key, val, step)
logger.dump(step=step)
# For atari the return reward is not the atari score
# so we have to get it from the infos dict
if is_atari and infos is not None and args.verbose >= 1:
episode_infos = infos[0].get("episode")
if episode_infos is not None:
print(f"Atari Episode Score: {episode_infos['r']:.2f}")
print("Atari Episode Length", episode_infos["l"])
if done and not is_atari and args.verbose > 0:
# NOTE: for env using VecNormalize, the mean reward
# is a normalized reward when `--norm_reward` flag is passed
print("Episode #{}, step#{}".format(ep_count, step))
print(f" Episode Reward: {episode_reward:.2f}")
print(" Episode Length", ep_len)
episode_rewards.append(episode_reward)
logger.record("eval/ep_len", ep_len, exclude='stdout')
logger.record("eval/ep_reward",
episode_reward,
exclude='stdout')
xlsx_logger.log('ep_len', ep_len)
xlsx_logger.log('reward', episode_reward)
logger.dump(step=step)
episode_lengths.append(ep_len)
episode_reward = 0.0
ep_len = 0
ep_count += 1
state = None
# Reset also when the goal is achieved when using HER
if done and infos[0].get("is_success") is not None:
if args.verbose > 1:
print("Success?", infos[0].get("is_success", False))
if infos[0].get("is_success") is not None:
successes.append(infos[0].get("is_success", False))
episode_reward, ep_len = 0.0, 0
ep_count += 1
# if (not args.no_render) and args.render_mode=='step':
# fig = env.render("human")
# else:
# fig = None
except KeyboardInterrupt:
pass
logger.dump(step=step)
xlsx_logger.close()
if args.verbose > 0 and len(successes) > 0:
print(f"Success rate: {100 * np.mean(successes):.2f}%")
if args.verbose > 0 and len(episode_rewards) > 0:
print(f"{len(episode_rewards)} Episodes")
print(
f"Mean reward: {np.mean(episode_rewards):.2f} +/- {np.std(episode_rewards):.2f}"
)
if args.verbose > 0 and len(episode_lengths) > 0:
print(
f"Mean episode length: {np.mean(episode_lengths):.2f} +/- {np.std(episode_lengths):.2f}"
)
env.close()
th.set_num_threads(num_threads)
is_atari = 'NoFrameskip' in env_id
stats_path = os.path.join(log_path, env_id)
hyperparams, stats_path = get_saved_hyperparams(stats_path,
norm_reward=norm_reward,
test_mode=True)
env_kwargs = {} if env_kwargs is None else env_kwargs
log_dir = reward_log if reward_log != '' else None
env = create_test_env(env_id,
n_envs=n_envs,
stats_path=stats_path,
seed=seed,
log_dir=log_dir,
should_render=not no_render,
hyperparams=hyperparams,
env_kwargs=env_kwargs)
model = ALGOS[algo].load(model_path, env=env, device=device)
obs = env.reset()
rm = AtariRewardModel(env, device)
rm.load_state_dict(
th.load(f"../reward-models/BreakoutNoFrameskip-v4-reward_model.pt"))
rm = rm.to(device)
random.seed(0)
np.random.seed(0)
with open(args_path, "r") as f:
# pytype: disable=module-attr
loaded_args = yaml.load(f, Loader=yaml.UnsafeLoader)
if loaded_args["env_kwargs"] is not None:
env_kwargs = loaded_args["env_kwargs"]
# overwrite with command line arguments
if args.env_kwargs is not None:
env_kwargs.update(args.env_kwargs)
log_dir = args.reward_log if args.reward_log != "" else None
env = create_test_env(
env_id,
n_envs=args.n_envs,
stats_path=stats_path,
seed=args.seed,
log_dir=log_dir,
should_render=args.render,
hyperparams=hyperparams,
env_kwargs=env_kwargs,
)
kwargs = dict(seed=args.seed)
if algo in off_policy_algos:
# Dummy buffer size as we don't need memory to enjoy the trained agent
kwargs.update(dict(buffer_size=1))
model = ALGOS[algo].load(model_path, env=env, **kwargs)
obs = env.reset()
# Force deterministic for DQN, DDPG, SAC and HER (that is a wrapper around)