def create_env(env_name, normalized, Training=False):
env = gym.make(env_name)
if normalized:
from stable_baselines.common.vec_env import VecNormalize, DummyVecEnv
vec_env = DummyVecEnv([lambda: env])
vec_env = VecNormalize.load('data/models/env_stats/'+env_name+'.pkl',
venv=vec_env)
vec_env.training = Training
vec_env.reward_range = env.reward_range
return env
def main():
args = get_args()
choose_device(args.device)
set_global_seeds(args.seed)
env_id = args.env
exp_id = args.exp_id
algo = args.algo
env_name = env_id[:-3]
env_index = env_list.index(env_id)
# Pass CustomEnv arguments: follow this for your CustomEnv if reward not known prior to training
env_kwargs = {} if args.env_kwargs is None else args.env_kwargs
if (args.env_kwargs is not None) and (env_id in ['AirSim-v0']):
if 'rew_land' in env_kwargs:
if (int(env_kwargs['rew_land']) in [500, 1000, 10000]):
env_success[-1] = int(env_kwargs['rew_land'])
else:
raise ValueError(
'Given env reward not acceptable. Please try again')
params = [exp_id, env_name.lower()]
folder = [exp_id, env_name.lower(), args.algo.lower()]
tensorboard_path, monitor_path, callback_path = None, None, None
if args.tensorboard:
tensorboard_path = "tensorboard/{}_{}".format(*params)
make_dir(tensorboard_path)
# if args.train_RL: # Begin training here (location of this condition also decides experiment performance)
# Load hyperparameters from yaml file
with open('hyperparams/{}.yml'.format(args.algo), 'r') as f:
hyperparams_dict = yaml.safe_load(f)
if env_id in list(hyperparams_dict.keys()):
hyperparams = hyperparams_dict[env_id]
else:
raise ValueError("Hyperparameters not found for {}-{}".format(
args.algo, env_id))
if args.hyperparams is not None:
# Overwrite hyperparams if needed
hyperparams.update(args.hyperparams)
# OPTIONAL: Print saved hyperparams
saved_hyperparams = OrderedDict([(key, hyperparams[key])
for key in sorted(hyperparams.keys())])
if args.verbose > 0:
pprint(saved_hyperparams)
if args.n_envs > 1:
# if args.verbose:
print("Overwriting n_envs with n={}".format(args.n_envs))
n_envs = args.n_envs
else:
n_envs = hyperparams.get('n_envs', 1)
# choose Monitor log path according to multiprocessing setting
if args.monitor:
if n_envs == 1:
monitor_path = 'logs/single/{}_{}_{}'.format(*folder)
else:
if algo not in ['dqn', 'her', 'sac', 'td3']:
monitor_path = 'logs/multi/{}_{}_{}'.format(*folder)
make_dir(monitor_path)
if int(float(args.timesteps_RL)) > 0:
# if args.verbose:
print("Overwriting n_timesteps with n={}".format(
int(float(args.timesteps_RL))))
n_timesteps = int(float(args.timesteps_RL))
else:
n_timesteps = int(hyperparams['n_timesteps'])
# Convert to python object if needed
if 'policy_kwargs' in hyperparams.keys() and isinstance(
hyperparams['policy_kwargs'], str):
hyperparams['policy_kwargs'] = eval(hyperparams['policy_kwargs'])
if 'n_envs' in hyperparams.keys():
del hyperparams['n_envs']
del hyperparams['n_timesteps'] #To avoid error
env_wrapper = get_wrapper_class(hyperparams)
if 'env_wrapper' in hyperparams.keys():
del hyperparams['env_wrapper']
# if (algo=='ppo2' and ('learning_rate' in hyperparams.keys())):
# hyperparams['learning_rate'] = linear_schedule(hyperparams['learning_rate'])
def create_env(n_envs, eval_env=False):
if algo in ['a2c', 'acer', 'acktr', 'ppo2']:
if n_envs > 1:
env = SubprocVecEnv([
make_env(env_id,
i,
args.seed,
log_dir=monitor_path,
wrapper_class=env_wrapper,
env_kwargs=env_kwargs) for i in range(n_envs)
])
else:
env = DummyVecEnv([
make_env(env_id,
0,
args.seed,
log_dir=monitor_path,
wrapper_class=env_wrapper,
env_kwargs=env_kwargs)
])
env = DummyVecEnv([lambda: gym.make(env_id, **env_kwargs)])
if env_wrapper is not None:
env = env_wrapper(env)
elif ((algo in ['dqn', 'her', 'sac', 'td3']) and n_envs > 1):
raise ValueError(
"Error: {} does not support multiprocessing!".format(algo))
elif ((algo in ['ddpg', 'ppo1', 'trpo', 'gail']) and n_envs > 1):
raise ValueError(
"Error: {} uses MPI for multiprocessing!".format(algo))
else:
env = make_vec_env(env_id,
n_envs=n_envs,
seed=args.seed,
monitor_dir=monitor_path,
wrapper_class=env_wrapper,
env_kwargs=env_kwargs)
if args.normalize: # choose from multiple options
# env = VecNormalize(env, clip_obs=np.inf)
env = VecNormalize(env, norm_reward=False, clip_obs=np.inf)
# env = VecNormalize(env, norm_reward=False, clip_obs=np.inf, **normalize_kwargs)
return env
# Zoo: env = SubprocVecEnv([make_env(env_id, i, seed, log_dir, wrapper_class=env_wrapper, env_kwargs=env_kwargs) for i in range(n_envs)])
# Zoo: env = DummyVecEnv([make_env(env_id, 0, seed, log_dir, wrapper_class=env_wrapper, env_kwargs=env_kwargs)])
env = create_env(n_envs)
# if args.train_RL: # checking impact of the if-condition position on experiment reproducibility
callback, callback_path = [], "callbacks/{}_{}_{}".format(*folder)
save_freq, eval_freq = 100 * episode_len[env_index], 100 * episode_len[
env_index]
save_freq, eval_freq = max(save_freq // n_envs,
1), max(eval_freq // n_envs, 1)
make_dir(callback_path)
if args.check_callback:
callback.append(
CheckpointCallback(save_freq=save_freq,
save_path=callback_path,
name_prefix='rl_model',
verbose=1))
if args.eval_callback:
callback.append(
EvalCallback(create_env(1, eval_env=True),
best_model_save_path=callback_path,
log_path=callback_path,
eval_freq=eval_freq,
verbose=1))
model = (algo_list[args.algo])(env=env,
seed=args.seed,
tensorboard_log=tensorboard_path,
n_cpu_tf_sess=1,
verbose=args.verbose,
**hyperparams)
print('\nTraining {} on {} now... \n'.format(algo, env_id))
start_time = time.time()
model.learn(total_timesteps=n_timesteps, callback=callback)
total_time = time.time() - start_time
if args.normalize:
env.save(os.path.join(callback_path, "vec_normalize.pkl"))
if n_envs > 1 or (algo in ['ddpg', 'trpo', 'gail']):
print("Took {:.2f}s for multiprocessed version - {:.2f} FPS".format(
total_time, n_timesteps / total_time))
else:
print("Took {:.2f}s for single process version - {:.2f} FPS".format(
total_time, n_timesteps / total_time))
env = DummyVecEnv([make_env(env_id, 0, args.seed, env_kwargs=env_kwargs)])
if args.normalize:
env = VecNormalize.load(
os.path.join(callback_path, "vec_normalize.pkl"), env)
env.training = False
env.norm_reward = False
env.seed(args.seed)
# Evaluate RL model - choose either best model or last available model
model = (algo_list[algo]).load(os.path.join(callback_path, 'best_model'))
# model = (algo_list[algo]).load("models/{}_{}_{}".format(*folder))
model.set_env(env)
evaluate('policy', model, env_id, env, algo, 100)
if args.monitor:
results_plotter.plot_results([monitor_path], n_timesteps,
results_plotter.X_TIMESTEPS,
"{} {}".format(algo, env_id))
plot_results(monitor_path)
if args.test:
print('\nTesting policy...\n')
obs = env.reset()
for _ in range(n_timesteps):
action, _states = model.predict(obs, deterministic=True)
if isinstance(env.action_space, gym.spaces.Box):
action = np.clip(action, env.action_space.low,
env.action_space.high)
obs, rewards, dones, info = env.step(action)
episode_reward += rewards
env.render()
if dones:
done_count += 1
success_count = check_success(env_index, env_success,
success_count)
total_reward += episode_reward
episode_reward = 0
env.reset()
print('\n{}/{} successful episodes'.format(success_count, done_count))
average_reward = total_reward / done_count
print('\nAverage reward: {}'.format(average_reward))
env.close()
def normalize_env(
env,
orig_log_dir,
sb_version,
vectorize=True,
continue_learning=False,
evaluate=False,
evaluate_during_learning=False,
normalize_kwargs=None,
):
if vectorize:
env = DummyVecEnv([lambda: env])
logger.debug("Normalize: {}".format(normalize_kwargs))
if evaluate:
# FIXME in continue learning training should be True so that we update the running average of obs and
# rewards with new samples; if I do that, the algo performs very poorly even with no changes in the env
if sb_version == "sb3":
env = VecNormalize3(env, training=False, **normalize_kwargs)
else:
env = VecNormalize(env, training=False, **normalize_kwargs)
if not evaluate_during_learning or continue_learning:
if not os.path.exists(
os.path.join(orig_log_dir, "vecnormalize.pkl")):
env_name = get_env_name(env=env.unwrapped,
sb_version=sb_version)
index_last_separator = orig_log_dir.rindex("/")
new_orig_log_dir = os.path.join(
orig_log_dir[0:index_last_separator], "logs_" + env_name)
logger.debug(
"{} does not exist. Trying to search it in the original model directory {}"
.format(os.path.join(orig_log_dir, "vecnormalize.pkl"),
new_orig_log_dir))
assert os.path.exists(new_orig_log_dir), "{} does not exist"
assert os.path.exists(
os.path.join(new_orig_log_dir, "vecnormalize.pkl")), (
os.path.join(new_orig_log_dir, "vecnormalize.pkl") +
" does not exist")
logger.debug("[evaluate] Loading {}".format(
os.path.join(new_orig_log_dir, "vecnormalize.pkl")))
if sb_version == "sb3":
env = VecNormalize3.load(
os.path.join(new_orig_log_dir, "vecnormalize.pkl"),
env)
else:
env = VecNormalize.load(
os.path.join(new_orig_log_dir, "vecnormalize.pkl"),
env)
else:
logger.debug("[evaluate] Loading {}".format(
os.path.join(orig_log_dir, "vecnormalize.pkl")))
if sb_version == "sb3":
env = VecNormalize3.load(
os.path.join(orig_log_dir, "vecnormalize.pkl"), env)
else:
env = VecNormalize.load(
os.path.join(orig_log_dir, "vecnormalize.pkl"), env)
# Deactivate training and reward normalization
env.training = False
env.norm_reward = False
elif continue_learning:
# FIXME: don't know why but during continue learning I have to disable training otherwise performance
# is not the same as in the model trained from scratch even without changing the params of the environment.
# in rl-baselines-zoo this is not done during continue learning:
# https://github.com/araffin/rl-baselines-zoo/blob/master/train.py#L365
if sb_version == "sb3":
env = VecNormalize3(env, training=False, **normalize_kwargs)
else:
env = VecNormalize(env, training=False, **normalize_kwargs)
assert os.path.exists(os.path.join(
orig_log_dir, "vecnormalize.pkl")), (
os.path.join(orig_log_dir, "vecnormalize.pkl") +
" does not exist")
logger.debug("[continue_learning] Loading {}".format(
os.path.join(orig_log_dir, "vecnormalize.pkl")))
if sb_version == "sb3":
env = VecNormalize3.load(
os.path.join(orig_log_dir, "vecnormalize.pkl"), env)
else:
env = VecNormalize.load(
os.path.join(orig_log_dir, "vecnormalize.pkl"), env)
else:
if sb_version == "sb3":
env = VecNormalize3(env, **normalize_kwargs)
else:
env = VecNormalize(env, **normalize_kwargs)
return env
if __name__ == '__main__':
log_dir = 'models/hover/empty_world_small/finalVec'
stats_path = os.path.join(log_dir, "vec_normalize.pkl")
env_id = 'CrazyflieObstacleEval-v0'
# Load the agent
model = PPO2.load(log_dir + '/ppo2_final')
# Load the saved statistics
env = DummyVecEnv([
lambda: gym.make(env_id, n_obstacles=1, avoidance_method='Heuristic')
])
env = VecNormalize.load(stats_path, env)
# do not update them at test time
env.training = False
# reward normalization is not needed at test time
env.norm_reward = False
eval_episodes = 50
total_goals_reached = 0
total_collisions = 0
total_flips = 0
total_steps_exceeded = 0
total_potential_collisions = 0
total_collisions_avoided = 0
total_timsteps = 0
# Observe trained agent
for i_episode in range(eval_episodes):