type=int)
parser.add_argument('-vae',
'--vae-path',
help='Path to saved VAE',
type=str,
default='')
args = parser.parse_args()
algo = args.algo
folder = args.folder
model = None
vae = None
if algo != '':
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)
model_path = "{}/{}.pkl".format(log_path, ENV_ID)
assert os.path.isdir(log_path), "The {} folder was not found".format(
log_path)
assert os.path.isfile(
model_path), "No model found for {} on {}, path: {}".format(
skip_eval = len(x) > 0
except (json.JSONDecodeError, pd.errors.EmptyDataError, TypeError):
pass
if skip_eval:
print("Skipping eval...")
else:
return_code = subprocess.call(["python", "enjoy.py"] + arguments)
if return_code != 0:
print("Error during evaluation, skipping...")
continue
x, y = ts2xy(load_results(reward_log), "timesteps")
if len(x) > 0:
# Retrieve training timesteps from config
exp_id = get_latest_run_id(os.path.join(args.log_dir, algo), env_id)
log_path = os.path.join(args.log_dir, algo, f"{env_id}_{exp_id}",
env_id)
hyperparams, _ = get_saved_hyperparams(log_path)
# Hack to format it properly
if hyperparams["n_timesteps"] < 1e6:
n_training_timesteps = f"{int(hyperparams['n_timesteps'] / 1e3)}k"
else:
n_training_timesteps = f"{int(hyperparams['n_timesteps'] / 1e6)}M"
mean_reward = np.mean(y)
std_reward = np.std(y)
results["algo"].append(algo)
results["env_id"].append(env_id)
results["mean_reward"].append(mean_reward)
results["std_reward"].append(std_reward)
for key in sorted(hyperparams.keys())])
# save vae path
saved_hyperparams['vae_path'] = args.vae_path
if vae is not None:
saved_hyperparams['z_size'] = vae.z_size
# Save simulation params
for key in SIM_PARAMS:
saved_hyperparams[key] = eval(key)
pprint(saved_hyperparams)
# Compute and create log path
log_path = os.path.join(args.log_folder, args.algo)
save_path = os.path.join(
log_path, "{}_{}".format(ENV_ID,
get_latest_run_id(log_path, ENV_ID) + 1))
params_path = os.path.join(save_path, ENV_ID)
os.makedirs(params_path, exist_ok=True)
# Create learning rate schedules for ppo2 and sac
if args.algo in ["ppo2", "sac"]:
for key in ['learning_rate', 'cliprange']:
if key not in hyperparams:
continue
if isinstance(hyperparams[key], str):
schedule, initial_value = hyperparams[key].split('_')
initial_value = float(initial_value)
hyperparams[key] = linear_schedule(initial_value)
elif isinstance(hyperparams[key], float):
hyperparams[key] = constfn(hyperparams[key])
else:
# Load hyperparameters from yaml file
with open('hyperparams/{}.yml'.format(args.algo), 'r') as f:
hyperparams = yaml.load(f)[BASE_ENV]
# Sort hyperparams that will be saved
saved_hyperparams = OrderedDict([(key, hyperparams[key]) for key in sorted(hyperparams.keys())])
# save vae path
saved_hyperparams['vae_path'] = args.vae_path
if vae is not None:
saved_hyperparams['z_size'] = vae.z_size
# Compute and create log path
log_path = os.path.join(args.log_folder, args.algo)
save_path = os.path.join(log_path, "{}_{}".format(ENV_ID, get_latest_run_id(log_path, ENV_ID) + 1))
params_path = os.path.join(save_path, ENV_ID)
os.makedirs(params_path, exist_ok=True)
# Create learning rate schedules for ppo2 and sac
if args.algo in ["ppo2", "sac"]:
for key in ['learning_rate', 'cliprange']:
if key not in hyperparams:
continue
if isinstance(hyperparams[key], str):
schedule, initial_value = hyperparams[key].split('_')
initial_value = float(initial_value)
hyperparams[key] = linear_schedule(initial_value)
elif isinstance(hyperparams[key], float):
hyperparams[key] = constfn(hyperparams[key])
else:
# Sort hyperparams that will be saved
saved_hyperparams = OrderedDict([(key, hyperparams[key]) for key in sorted(hyperparams.keys())])
# save vae path
saved_hyperparams['vae_path'] = args.vae_path
if vae is not None:
saved_hyperparams['z_size'] = vae.z_size
# Save simulation params
for key in SIM_PARAMS:
saved_hyperparams[key] = eval(key)
saved_hyperparams['seed'] = args.seed
pprint(saved_hyperparams)
# Compute and create log path
log_path = os.path.join(args.log_folder, args.algo)
save_path = os.path.join(log_path, "{}_{}".format(ID_ENV+'_'+str(SIZE_Z), get_latest_run_id(log_path, ID_ENV) + 1))
params_path = os.path.join(save_path, ID_ENV)
os.makedirs(params_path, exist_ok=True)
# Create learning rate schedules for ppo2 and sac
if args.algo in ["ppo2", "sac"]:
for key in ['learning_rate', 'cliprange']:
if key not in hyperparams:
continue
if isinstance(hyperparams[key], str):
schedule, initial_value = hyperparams[key].split('_')
initial_value = float(initial_value)
hyperparams[key] = linear_schedule(initial_value)
elif isinstance(hyperparams[key], float):
hyperparams[key] = constfn(hyperparams[key])
else:
def record_video(env_id: str = "CartPole-v1",
algo: str = "ppo",
folder: str = "rl-trained-agents",
video_folder: str = "logs/videos/",
video_length: int = 1000,
n_envs: int = 1,
deterministic: bool = False,
seed: int = 0,
no_render: bool = False,
exp_id: int = 0):
if exp_id == 0:
exp_id = get_latest_run_id(os.path.join(folder, algo), env_id)
print(f"Loading latest experiment, id={exp_id}")
# Sanity checks
if exp_id > 0:
log_path = os.path.join(folder, algo, f"{env_id}_{exp_id}")
else:
log_path = os.path.join(folder, algo)
model_path = os.path.join(log_path, f"{env_id}.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 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,
name_prefix=f"{algo}-{env_id}",
)
env.reset()
for _ in range(video_length + 1):
action, _ = model.predict(obs, deterministic=deterministic)
obs, _, _, _ = env.step(action)
# Workaround for https://github.com/openai/gym/issues/893
if n_envs == 1 and "Bullet" not in env_id and not is_atari:
env = env.venv
# DummyVecEnv
while isinstance(env, VecEnvWrapper):
env = env.venv
if isinstance(env, DummyVecEnv):
env.envs[0].env.close()
else:
env.close()
else:
# SubprocVecEnv
env.close()
