log_path = "{}/{}/".format(args.log_folder, args.algo)
if exp_name:
assert (not ('_' in exp_name)), 'experiment name should not include _'
save_path = os.path.join(
log_path,
"{}_{}_{}".format(env_id, exp_name,
get_latest_run_id(log_path, env_id, exp_name) + 1))
else:
save_path = os.path.join(
log_path, "{}_{}".format(env_id,
get_latest_run_id(log_path, env_id) + 1))
if args.log_outputs:
# Log the outputs
logger.configure(folder=save_path, format_strs=['log'])
params_path = "{}/{}".format(save_path, env_id)
os.makedirs(params_path, exist_ok=True)
tensorboard_log = None if args.no_tensorboard else save_path
monitor_log = None if args.no_monitor else save_path
is_atari = 'NoFrameskip' in env_id
print("=" * 10, env_id, "=" * 10)
# Load hyperparameters from yaml file
with open('hyperparams/{}.yml'.format(args.algo), 'r') as f:
hyperparams_dict = yaml.load(f)
if is_atari:
hyperparams = hyperparams_dict['atari']
def main(args):
rank = MPI.COMM_WORLD.Get_rank()
model_dir = os.path.join(config.MODELDIR, args.env_name)
if rank == 0:
try:
os.makedirs(model_dir)
except:
pass
if args.reset:
reset_files(model_dir)
logger.configure(config.LOGDIR)
else:
logger.configure(format_strs=[])
if args.debug:
logger.set_level(config.DEBUG)
else:
time.sleep(5)
logger.set_level(config.INFO)
workerseed = args.seed + 10000 * MPI.COMM_WORLD.Get_rank()
set_global_seeds(workerseed)
logger.info('\nSetting up the selfplay training environment opponents...')
base_env = get_environment(args.env_name)
env = selfplay_wrapper(base_env)(opponent_type=args.opponent_type,
verbose=args.verbose)
env.seed(workerseed)
CustomPolicy = get_network_arch(args.env_name)
params = {
'gamma': args.gamma,
'timesteps_per_actorbatch': args.timesteps_per_actorbatch,
'clip_param': args.clip_param,
'entcoeff': args.entcoeff,
'optim_epochs': args.optim_epochs,
'optim_stepsize': args.optim_stepsize,
'optim_batchsize': args.optim_batchsize,
'lam': args.lam,
'adam_epsilon': args.adam_epsilon,
'schedule': 'linear',
'verbose': 1,
'tensorboard_log': config.LOGDIR
}
time.sleep(
5
) # allow time for the base model to be saved out when the environment is created
if args.reset or not os.path.exists(
os.path.join(model_dir, 'best_model.zip')):
logger.info('\nLoading the base PPO agent to train...')
model = PPO1.load(os.path.join(model_dir, 'base.zip'), env, **params)
else:
logger.info(
'\nLoading the best_model.zip PPO agent to continue training...')
model = PPO1.load(os.path.join(model_dir, 'best_model.zip'), env,
**params)
#Callbacks
logger.info(
'\nSetting up the selfplay evaluation environment opponents...')
callback_args = {
'eval_env':
selfplay_wrapper(base_env)(opponent_type=args.opponent_type,
verbose=args.verbose),
'best_model_save_path':
config.TMPMODELDIR,
'log_path':
config.LOGDIR,
'eval_freq':
args.eval_freq,
'n_eval_episodes':
args.n_eval_episodes,
'deterministic':
False,
'render':
True,
'verbose':
0
}
if args.rules:
logger.info(
'\nSetting up the evaluation environment against the rules-based agent...'
)
# Evaluate against a 'rules' agent as well
eval_actual_callback = EvalCallback(
eval_env=selfplay_wrapper(base_env)(opponent_type='rules',
verbose=args.verbose),
eval_freq=1,
n_eval_episodes=args.n_eval_episodes,
deterministic=args.best,
render=True,
verbose=0)
callback_args['callback_on_new_best'] = eval_actual_callback
# Evaluate the agent against previous versions
eval_callback = SelfPlayCallback(args.opponent_type, args.threshold,
args.env_name, **callback_args)
logger.info('\nSetup complete - commencing learning...\n')
model.learn(total_timesteps=int(1e9),
callback=[eval_callback],
reset_num_timesteps=False,
tb_log_name="tb")
env.close()
del env
"Cornwall", "Plymouth", "Torbay", "East Devon", "Exeter", "Mid Devon",
"North Devon", "South Hams", "Teignbridge", "Torridge", "West Devon"
]
env = make("SEIRmulti-v0")
districts_group_ids = [
env.unwrapped.district_idx(name) for name in districts_group
]
env = NormalizedObservationWrapper(env)
env = NormalizedRewardWrapper(env)
env = MultiAgentSelectObservation(env, districts_group_ids)
env = MultiAgentSelectAction(env, districts_group_ids, 1)
env = MultiAgentSelectReward(env, districts_group_ids)
logger.configure(folder=args.monitor_path, format_strs=["csv"])
env = DummyVecEnv([lambda: env])
print(f"tensorboard --logdir {args.monitor_path}")
layers = [args.n_hidden_units] * args.n_hidden_layers
model = PPO2(MlpPolicy,
env,
verbose=0,
tensorboard_log=args.monitor_path,
ent_coef=args.entropy_coef,
learning_rate=args.learning_rate,
noptepochs=args.n_epochs,
nminibatches=args.n_minibatches,
def run_experiment_with_trained(augment_num_timesteps, linear_co_threshold, augment_seed, augment_run_num, network_size,
policy_env, policy_num_timesteps, policy_run_num, policy_seed, eval_seed, eval_run_num, learning_rate,
additional_note, result_dir, keys_to_include, metric_param, linear_top_vars_list=None,
linear_correlation_neuron_list=None, visualize=False, lagrangian_inds_to_include=None,
neurons_inds_to_include=None, use_lagrangian=True):
trained_model = None
if not use_lagrangian:
with tf.variable_scope("trained_model"):
common_arg_parser = get_common_parser()
trained_args, cma_unknown_args = common_arg_parser.parse_known_args()
trained_args.env = policy_env
trained_args.seed = policy_seed
trained_args.num_timesteps = policy_num_timesteps
trained_args.run_num = policy_run_num
trained_this_run_dir = get_dir_path_for_this_run(trained_args)
trained_traj_params_dir_name = get_full_params_dir(trained_this_run_dir)
trained_save_dir = get_save_dir(trained_this_run_dir)
trained_final_file = get_full_param_traj_file_path(trained_traj_params_dir_name, "pi_final")
trained_final_params = pd.read_csv(trained_final_file, header=None).values[0]
trained_model = PPO2.load(f"{trained_save_dir}/ppo2", seed=augment_seed)
trained_model.set_pi_from_flat(trained_final_params)
args = AttributeDict()
args.normalize = True
args.num_timesteps = augment_num_timesteps
args.run_num = augment_run_num
args.alg = "ppo2"
args.seed = augment_seed
logger.log(f"#######TRAIN: {args}")
# non_linear_global_dict
timestamp = get_time_stamp('%Y_%m_%d_%H_%M_%S')
experiment_label = f"learning_rate_{learning_rate}timestamp_{timestamp}_augment_num_timesteps{augment_num_timesteps}" \
f"_top_num_to_include{linear_co_threshold.start}_{linear_co_threshold.stop}" \
f"_augment_seed{augment_seed}_augment_run_num{augment_run_num}_network_size{network_size}" \
f"_policy_num_timesteps{policy_num_timesteps}_policy_run_num{policy_run_num}_policy_seed{policy_seed}" \
f"_eval_seed{eval_seed}_eval_run_num{eval_run_num}_additional_note_{additional_note}"
if policy_env == "DartWalker2d-v1":
entry_point = 'gym.envs.dart:DartWalker2dEnv_aug_input'
elif policy_env == "DartHopper-v1":
entry_point = 'gym.envs.dart:DartHopperEnv_aug_input'
elif policy_env == "DartHalfCheetah-v1":
entry_point = 'gym.envs.dart:DartHalfCheetahEnv_aug_input'
elif policy_env == "DartSnake7Link-v1":
entry_point = 'gym.envs.dart:DartSnake7LinkEnv_aug_input'
else:
raise NotImplemented()
this_run_dir = get_experiment_path_for_this_run(entry_point, args.num_timesteps, args.run_num,
args.seed, learning_rate=learning_rate, top_num_to_include=linear_co_threshold,
result_dir=result_dir, network_size=network_size)
full_param_traj_dir_path = get_full_params_dir(this_run_dir)
log_dir = get_log_dir(this_run_dir)
save_dir = get_save_dir(this_run_dir)
create_dir_remove(this_run_dir)
create_dir_remove(full_param_traj_dir_path)
create_dir_remove(save_dir)
create_dir_remove(log_dir)
logger.configure(log_dir)
linear_top_vars_list_wanted_to_print = []
if (use_lagrangian and lagrangian_inds_to_include is None) or (not use_lagrangian and neurons_inds_to_include is None):
# note this is only linear
if linear_top_vars_list is None or linear_correlation_neuron_list is None:
linear_top_vars_list, linear_correlation_neuron_list = read_linear_top_var(policy_env, policy_num_timesteps, policy_run_num, policy_seed, eval_seed,
eval_run_num, additional_note, metric_param=metric_param)
lagrangian_inds_to_include, neurons_inds_to_include, linear_top_vars_list_wanted_to_print = \
get_wanted_lagrangians_and_neurons(keys_to_include, linear_top_vars_list, linear_correlation_neuron_list, linear_co_threshold)
with open(f"{log_dir}/lagrangian_inds_to_include.json", 'w') as fp:
json.dump(lagrangian_inds_to_include, fp)
with open(f"{log_dir}/linear_top_vars_list_wanted_to_print.json", 'w') as fp:
json.dump(linear_top_vars_list_wanted_to_print, fp)
with open(f"{log_dir}/neurons_inds_to_include.json", 'w') as fp:
json.dump(neurons_inds_to_include, fp)
args.env = f'{experiment_label}_{entry_point}-v1'
if not use_lagrangian:
register(
id=args.env,
entry_point=entry_point,
max_episode_steps=1000,
kwargs={"lagrangian_inds_to_include": None, "trained_model": trained_model,
"neurons_inds_to_include": neurons_inds_to_include}
)
else:
register(
id=args.env,
entry_point=entry_point,
max_episode_steps=1000,
kwargs={"lagrangian_inds_to_include": lagrangian_inds_to_include, "trained_model": None,
"neurons_inds_to_include": None}
)
def make_env():
env_out = gym.make(args.env)
env_out.env.visualize = visualize
env_out = bench.Monitor(env_out, logger.get_dir(), allow_early_resets=True)
return env_out
env = DummyVecEnv([make_env])
walker_env = env.envs[0].env.env
walker_env.disableViewer = not visualize
if args.normalize:
env = VecNormalize(env)
policy = MlpPolicy
set_global_seeds(args.seed)
walker_env.seed(args.seed)
num_dof = walker_env.robot_skeleton.ndofs
show_M_matrix(num_dof, lagrangian_inds_to_include, linear_co_threshold, log_dir)
# extra run info I added for my purposes
run_info = {"run_num": args.run_num,
"env_id": args.env,
"full_param_traj_dir_path": full_param_traj_dir_path}
layers = [network_size, network_size]
policy_kwargs = {"net_arch" : [dict(vf=layers, pi=layers)]}
model = PPO2(policy=policy, env=env, n_steps=4096, nminibatches=64, lam=0.95, gamma=0.99,
noptepochs=10,
ent_coef=0.0, learning_rate=learning_rate, cliprange=0.2, optimizer='adam', policy_kwargs=policy_kwargs,
seed=args.seed)
model.tell_run_info(run_info)
model.learn(total_timesteps=args.num_timesteps, seed=args.seed)
model.save(f"{save_dir}/ppo2")
if args.normalize:
env.save_running_average(save_dir)
return log_dir
from stable_baselines.common.base_class import _UnvecWrapper
from utils import make_env, ALGOS, linear_schedule, get_latest_run_id, get_wrapper_class
from utils.hyperparams_opt import hyperparam_optimization
from utils.callbacks import SaveVecNormalizeCallback
from utils.noise import LinearNormalActionNoise
from utils.utils import StoreDict
from stable_baselines.logger import configure
from utils.callbacks import GoalToleranceCallback
# Export the following environment variables for csv files
# export OPENAI_LOG_FORMAT='stdout,log,csv'
# export OPENAI_LOGDIR="log_dir"
configure()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default="CartPole-v1", help='environment ID')
parser.add_argument('-tb', '--tensorboard-log', help='Tensorboard log dir', default='', type=str)
parser.add_argument('-i', '--trained-agent', help='Path to a pretrained agent to continue training',
default='', type=str)
parser.add_argument('--algo', help='RL Algorithm', default='ppo2',
type=str, required=False, choices=list(ALGOS.keys()))
parser.add_argument('-n', '--n-timesteps', help='Overwrite the number of timesteps', default=-1,
type=int)
parser.add_argument('--log-interval', help='Override log interval (default: -1, no change)', default=-1,
type=int)
parser.add_argument('--eval-freq', help='Evaluate the agent every n steps (if negative, no evaluation)',
default=10000, type=int)
else:
action, value, neglogp = self.sess.run(
[self.action, self.value_flat, self.neglogp],
{self.obs_ph: obs})
return action, value, self.initial_state, neglogp
def proba_step(self, obs, state=None, mask=None):
return self.sess.run(self.policy_proba, {self.obs_ph: obs})
def value(self, obs, state=None, mask=None):
return self.sess.run(self.value_flat, {self.obs_ph: obs})
if __name__ == '__main__':
saver = U.ConfigurationSaver(log_dir='./logs')
logger.configure(folder=saver.data_dir)
env = gym.make('gym_docking:docking-v3')
env = VecNormalize(env, norm_obs=True, norm_reward=False, clip_obs=255)
checkpoint_callback = CheckpointCallback(
save_freq=int(5e4),
save_path='./logs/',
name_prefix='rl_model_621_shaping_video_10M')
model = PPO2(
policy=MlpPolicy,
env=env,
verbose=1,
tensorboard_log="./ppo2_docking_tensorboard/",
lam=0.95,
def configure_logger(log_path, **kwargs):
if log_path is not None:
logger.configure(log_path)
else:
logger.configure(**kwargs)
def do_ppo(args, start_pi_theta, parent_this_run_dir, full_space_save_dir):
"""
Runs the test
"""
logger.log(f"#######CMA and then PPO TRAIN: {args}")
this_conti_ppo_run_dir = get_ppo_part(parent_this_run_dir)
log_dir = get_log_dir(this_conti_ppo_run_dir)
conti_ppo_save_dir = get_save_dir(this_conti_ppo_run_dir)
logger.configure(log_dir)
full_param_traj_dir_path = get_full_params_dir(this_conti_ppo_run_dir)
if os.path.exists(full_param_traj_dir_path):
import shutil
shutil.rmtree(full_param_traj_dir_path)
os.makedirs(full_param_traj_dir_path)
if os.path.exists(conti_ppo_save_dir):
import shutil
shutil.rmtree(conti_ppo_save_dir)
os.makedirs(conti_ppo_save_dir)
def make_env():
env_out = gym.make(args.env)
env_out.env.disableViewer = True
env_out.env.visualize = False
env_out = bench.Monitor(env_out,
logger.get_dir(),
allow_early_resets=True)
return env_out
env = DummyVecEnv([make_env])
if args.normalize:
env = VecNormalize(env)
model = PPO2.load(f"{full_space_save_dir}/ppo2") # load after V
model.set_pi_from_flat(
start_pi_theta
) # don't set Vf's searched from CMA, those weren't really tested.
if args.normalize:
env.load_running_average(full_space_save_dir)
model.set_env(env)
run_info = {
"run_num": args.run_num,
"env_id": args.env,
"full_param_traj_dir_path": full_param_traj_dir_path
}
# model = PPO2(policy=policy, env=env, n_steps=args.n_steps, nminibatches=args.nminibatches, lam=0.95, gamma=0.99,
# noptepochs=10,
# ent_coef=0.0, learning_rate=3e-4, cliprange=0.2, optimizer=args.optimizer)
model.tell_run_info(run_info)
episode_returns = model.learn(total_timesteps=args.ppo_num_timesteps)
model.save(f"{conti_ppo_save_dir}/ppo2")
env.save_running_average(conti_ppo_save_dir)
return episode_returns, full_param_traj_dir_path
# Make the training environment
env = make_train_env(datapaths)
# Make the testing environments
eval_envs = {}
for d in test_datasets:
path = os.path.join('./data', d + '.csv')
output_path = os.path.join(anomaly_curve_log, d + '.csv')
csv_file, csv_writer = generate_csv_writer(output_path)
eval_envs[d] = {
'env': make_eval_env(datapath=path, budget=args.budget),
'csv_writer': csv_writer,
'csv_file': csv_file,
'mean_reward': 0,
}
# Train the model
model = PPO2('MlpPolicy', env, verbose=1)
model.set_eval(eval_envs, args.eval_log_interval)
model.learn(total_timesteps=args.num_timesteps,
log_interval=args.rl_log_interval)
model.save(os.path.join(args.log, 'model'))
if __name__ == "__main__":
parser = argsparser()
args = parser.parse_args()
logger.configure(args.log)
train(args)
from stable_baselines.ppo2 import PPO2
from stable_baselines import TD3
from stable_baselines.td3.policies import MlpPolicy
from stable_baselines.ddpg.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise
from stable_baselines import logger
from stable_baselines.common.callbacks import EvalCallback
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines.bench import Monitor
NUM_TIMESTEPS = int(2e7)
SEED = 721
EVAL_FREQ = 1e6
EVAL_EPISODES = 100
LOGDIR = "tank_td3" # moved to zoo afterwards.
logger.configure(folder=LOGDIR)
env = gym.make("TankGym-v0")
env.seed(SEED)
env.policy = tankgym.BaselineRand()
# The noise objects for TD3
n_actions = env.action_space.shape[-1]
action_noise = NormalActionNoise(mean=np.zeros(n_actions), sigma=0.1 * np.ones(n_actions))
model = TD3(MlpPolicy, env, action_noise=action_noise, verbose=1)
# eval_callback = EvalCallback(env, best_model_save_path=LOGDIR, log_path=LOGDIR, eval_freq=EVAL_FREQ, n_eval_episodes=EVAL_EPISODES)
model.learn(total_timesteps=NUM_TIMESTEPS, log_interval=10)
def main():
parser = arg_parser()
add_env_params(parser)
parser.add_argument('--num-timesteps', type=int, default=int(1e12))
parser.add_argument('--num_env', type=int, default=32)
parser.add_argument('--use_news', type=int, default=0)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--gamma_ext', type=float, default=0.99)
parser.add_argument('--lam', type=float, default=0.95)
parser.add_argument('--update_ob_stats_every_step', type=int, default=0)
parser.add_argument('--update_ob_stats_independently_per_gpu',
type=int,
default=0)
parser.add_argument('--update_ob_stats_from_random_agent',
type=int,
default=1)
parser.add_argument('--proportion_of_exp_used_for_predictor_update',
type=float,
default=1.)
parser.add_argument('--tag', type=str, default='')
parser.add_argument('--policy',
type=str,
default='rnn',
choices=['cnn', 'rnn'])
parser.add_argument('--int_coeff', type=float, default=1.)
parser.add_argument('--ext_coeff', type=float, default=2.)
parser.add_argument('--dynamics_bonus', type=int, default=0)
parser.add_argument(
'--save_dir',
type=str,
default='/home/hxu/PriorRL/random-network-distillation/ckpts/')
parser.add_argument(
'--load_dir',
type=str,
default='/home/hxu/PriorRL/random-network-distillation/ckpts/')
parser.add_argument('--test', type=int, default=0)
parser.add_argument('--save_image', type=int, default=0)
parser.add_argument('--exp_name', type=str, default='tmp')
parser.add_argument('--logdir', type=str, default='./logs/')
parser.add_argument('--clip_rewards', type=int, default=1)
parser.add_argument('--e_greedy', type=int, default=0)
parser.add_argument('--action_space', type=str, default='RIGHT_ONLY')
parser.add_argument('--load_mtype', type=str, default='latest')
args = parser.parse_args()
logdir = os.path.join(
args.logdir, args.exp_name + '_' +
datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S"))
logger.configure(folder=logdir,
format_strs=['stdout', 'log', 'csv']
if MPI.COMM_WORLD.Get_rank() == 0 else [])
if MPI.COMM_WORLD.Get_rank() == 0:
with open(os.path.join(logger.get_dir(), 'experiment_tag.txt'),
'w') as f:
f.write(args.tag)
# shutil.copytree(os.path.dirname(os.path.abspath(__file__)), os.path.join(logger.get_dir(), 'code'))
mpi_util.setup_mpi_gpus()
seed = 10000 * args.seed + MPI.COMM_WORLD.Get_rank()
set_global_seeds(seed)
hps = dict(frame_stack=4,
nminibatches=4,
nepochs=4,
lr=0.0001,
max_grad_norm=0.0,
use_news=args.use_news,
gamma=args.gamma,
gamma_ext=args.gamma_ext,
max_episode_steps=args.max_episode_steps,
lam=args.lam,
update_ob_stats_every_step=args.update_ob_stats_every_step,
update_ob_stats_independently_per_gpu=args.
update_ob_stats_independently_per_gpu,
update_ob_stats_from_random_agent=args.
update_ob_stats_from_random_agent,
proportion_of_exp_used_for_predictor_update=args.
proportion_of_exp_used_for_predictor_update,
policy=args.policy,
int_coeff=args.int_coeff,
ext_coeff=args.ext_coeff,
dynamics_bonus=args.dynamics_bonus)
tf_util.make_session(make_default=True)
train(env_id=args.env,
num_env=args.num_env,
seed=seed,
num_timesteps=args.num_timesteps,
hps=hps,
load_dir=args.load_dir,
save_dir=args.save_dir,
test=args.test,
exp_name=args.exp_name,
clip_rewards=args.clip_rewards,
save_image=args.save_image,
action_space=args.action_space,
e_greedy=args.e_greedy,
load_mtype=args.load_mtype)
def train(
_run,
_seed: int,
env_name: str,
rollout_path: str,
n_expert_demos: Optional[int],
log_dir: str,
*,
n_epochs: int,
n_gen_steps_per_epoch: int,
n_disc_steps_per_epoch: int,
init_trainer_kwargs: dict,
n_episodes_eval: int,
plot_interval: int,
n_plot_episodes: int,
show_plots: bool,
init_tensorboard: bool,
checkpoint_interval: int = 5,
) -> dict:
"""Train an adversarial-network-based imitation learning algorithm.
Plots (turn on using `plot_interval > 0`):
- Plot discriminator loss during discriminator training steps in blue and
discriminator loss during generator training steps in red.
- Plot the performance of the generator policy versus the performance of
a random policy. Also plot the performance of an expert policy if that is
provided in the arguments.
Checkpoints:
- DiscrimNets are saved to f"{log_dir}/checkpoints/{step}/discrim/",
where step is either the training epoch or "final".
- Generator policies are saved to
f"{log_dir}/checkpoints/{step}/gen_policy/".
Args:
_seed: Random seed.
env_name: The environment to train in.
rollout_path: Path to pickle containing list of Trajectories. Used as
expert demonstrations.
n_expert_demos: The number of expert trajectories to actually use
after loading them from `rollout_path`.
If None, then use all available trajectories.
If `n_expert_demos` is an `int`, then use exactly `n_expert_demos`
trajectories, erroring if there aren't enough trajectories. If there are
surplus trajectories, then use the
first `n_expert_demos` trajectories and drop the rest.
log_dir: Directory to save models and other logging to.
n_epochs: The number of epochs to train. Each epoch consists of
`n_disc_steps_per_epoch` discriminator steps followed by
`n_gen_steps_per_epoch` generator steps.
n_gen_steps_per_epoch: The number of generator update steps during every
training epoch.
n_disc_steps_per_epoch: The number of discriminator update steps during
every training epoch.
init_trainer_kwargs: Keyword arguments passed to `init_trainer`,
used to initialize the trainer.
n_episodes_eval: The number of episodes to average over when calculating
the average episode reward of the imitation policy for return.
plot_interval: The number of epochs between each plot. (If nonpositive,
then plots are disabled).
n_plot_episodes: The number of episodes averaged over when
calculating the average episode reward of a policy for the performance
plots.
show_plots: Figures are always saved to `f"{log_dir}/plots/*.png"`. If
`show_plots` is True, then also show plots as they are created.
init_tensorboard: If True, then write tensorboard logs to `{log_dir}/sb_tb`.
checkpoint_interval: Save the discriminator and generator models every
`checkpoint_interval` epochs and after training is complete. If <=0,
then only save weights after training is complete.
Returns:
A dictionary with two keys. "imit_stats" gives the return value of
`rollout_stats()` on rollouts test-reward-wrapped
environment, using the final policy (remember that the ground-truth reward
can be recovered from the "monitor_return" key). "expert_stats" gives the
return value of `rollout_stats()` on the expert demonstrations loaded from
`rollout_path`.
"""
tf.logging.info("Logging to %s", log_dir)
os.makedirs(log_dir, exist_ok=True)
sacred_util.build_sacred_symlink(log_dir, _run)
# Calculate stats for expert rollouts. Used for plot and return value.
with open(rollout_path, "rb") as f:
expert_trajs = pickle.load(f)
if n_expert_demos is not None:
assert len(expert_trajs) >= n_expert_demos
expert_trajs = expert_trajs[:n_expert_demos]
expert_stats = util.rollout.rollout_stats(expert_trajs)
with util.make_session():
sb_logger.configure(folder=osp.join(log_dir, 'generator'),
format_strs=['tensorboard', 'stdout'])
if init_tensorboard:
sb_tensorboard_dir = osp.join(log_dir, "sb_tb")
kwargs = init_trainer_kwargs
kwargs["init_rl_kwargs"] = kwargs.get("init_rl_kwargs", {})
kwargs["init_rl_kwargs"]["tensorboard_log"] = sb_tensorboard_dir
trainer = init_trainer(env_name,
expert_trajs,
seed=_seed,
log_dir=log_dir,
**init_trainer_kwargs)
if plot_interval > 0:
visualizer = _TrainVisualizer(
trainer=trainer,
show_plots=show_plots,
n_episodes_per_reward_data=n_plot_episodes,
log_dir=log_dir,
expert_mean_ep_reward=expert_stats["return_mean"])
else:
visualizer = None
# Main training loop.
for epoch in tqdm.tqdm(range(1, n_epochs + 1), desc="epoch"):
trainer.train_disc(n_disc_steps_per_epoch)
if visualizer:
visualizer.add_data_disc_loss(False)
trainer.train_gen(n_gen_steps_per_epoch)
if visualizer:
visualizer.add_data_disc_loss(True)
if visualizer and epoch % plot_interval == 0:
visualizer.plot_disc_loss()
visualizer.add_data_ep_reward(trainer.venv,
"Ground Truth Reward")
visualizer.add_data_ep_reward(trainer.venv_train,
"Train Reward")
visualizer.add_data_ep_reward(trainer.venv_test, "Test Reward")
visualizer.plot_ep_reward()
if checkpoint_interval > 0 and epoch % checkpoint_interval == 0:
save(trainer,
os.path.join(log_dir, "checkpoints", f"{epoch:05d}"))
# Save final artifacts.
save(trainer, os.path.join(log_dir, "checkpoints", "final"))
# Final evaluation of imitation policy.
results = {}
sample_until_eval = util.rollout.min_episodes(n_episodes_eval)
trajs = util.rollout.generate_trajectories(
trainer.gen_policy,
trainer.venv_test,
sample_until=sample_until_eval)
results["imit_stats"] = util.rollout.rollout_stats(trajs)
results["expert_stats"] = expert_stats
return results
def main(args):
logger.configure(config.LOGDIR)
if args.debug:
logger.set_level(config.DEBUG)
else:
logger.set_level(config.INFO)
#make environment
env = get_environment(args.env_name)(verbose = args.verbose, manual = args.manual)
env.seed(args.seed)
total_rewards = {}
if args.recommend:
ppo_model = load_model(env, 'best_model.zip')
ppo_agent = Agent('best_model', ppo_model)
else:
ppo_agent = None
agents = []
#load the agents
if len(args.agents) != env.n_players:
raise Exception(f'{len(args.agents)} players specified but this is a {env.n_players} player game!')
for i, agent in enumerate(args.agents):
if agent == 'human':
agent_obj = Agent('human')
elif agent== 'greedy':
agent_obj = Agent('greedy')
elif agent == 'rules':
agent_obj = Agent('rules')
elif agent == 'base':
base_model = load_model(env, 'base.zip')
agent_obj = Agent('base', base_model)
else:
ppo_model = load_model(env, f'{agent}.zip')
agent_obj = Agent(agent, ppo_model)
agents.append(agent_obj)
total_rewards[agent_obj.id] = 0
if args.env_name == "blobwar":
human_blobwar = Human()
#play games
logger.info(f'\nPlaying {args.games} games...')
for game in range(args.games):
players = agents[:]
if args.randomise_players:
random.shuffle(players)
obs = env.reset()
done = False
for i, p in enumerate(players):
logger.debug(f'Player {i+1} = {p.name}')
while not done:
current_player = players[env.current_player_num]
env.render()
logger.debug(f'Current player name: {current_player.name}')
if args.recommend and current_player.name in ['human', 'rules']:
# show recommendation from last loaded model
logger.debug(f'\nRecommendation by {ppo_agent.name}:')
action = ppo_agent.choose_action(env, choose_best_action = True, mask_invalid_actions = True)
if current_player.name == 'human':
if args.env_name == "blobwar":
move= human_blobwar.compute_next_move(env.core)
action=env.encode_action(move)
else:
action = input('\nPlease choose an action: ')
try:
action = int(action)
except:
# for MulitDiscrete action input as list TODO
action = eval(action)
elif current_player.name == 'rules':
logger.debug(f'\n{current_player.name} model choices')
action = current_player.choose_action(env, choose_best_action = False, mask_invalid_actions = True)
else:
logger.debug(f'\n{current_player.name} model choices')
action = current_player.choose_action(env, choose_best_action = args.best, mask_invalid_actions = True)
obs, reward, done, _ = env.step(action)
for r, player in zip(reward, players):
total_rewards[player.id] += r
player.points += r
if args.cont:
input('Press any key to continue')
env.render()
logger.info(f"Played {game + 1} games: {total_rewards}")
if args.write_results:
write_results(players, game, args.games, env.turns_taken)
for p in players:
p.points = 0
env.close()
def main():
args = parser().parse_args()
cfg = YAML().load(
open(os.environ["FLIGHTMARE_PATH"] + "/flightlib/configs/vec_env.yaml",
'r'))
if not args.train:
cfg["env"]["num_envs"] = 1
cfg["env"]["num_threads"] = 1
if args.render:
cfg["env"]["render"] = "yes"
else:
cfg["env"]["render"] = "no"
env = wrapper.FlightEnvVec(
QuadrotorEnv_v1(dump(cfg, Dumper=RoundTripDumper), False))
# set random seed
configure_random_seed(args.seed, env=env)
#
if args.train:
# save the configuration and other files
rsg_root = os.path.dirname(os.path.abspath(__file__))
log_dir = rsg_root + '/saved'
saver = U.ConfigurationSaver(log_dir=log_dir)
model = PPO2(
tensorboard_log=saver.data_dir,
policy=MlpPolicy, # check activation function
policy_kwargs=dict(net_arch=[dict(pi=[128, 128], vf=[128, 128])],
act_fun=tf.nn.relu),
env=env,
lam=0.95,
gamma=0.99, # lower 0.9 ~ 0.99
# n_steps=math.floor(cfg['env']['max_time'] / cfg['env']['ctl_dt']),
n_steps=250,
ent_coef=0.00,
learning_rate=3e-4,
vf_coef=0.5,
max_grad_norm=0.5,
nminibatches=1,
noptepochs=10,
cliprange=0.2,
verbose=1,
)
# tensorboard
# Make sure that your chrome browser is already on.
# TensorboardLauncher(saver.data_dir + '/PPO2_1')
# PPO run
# Originally the total timestep is 5 x 10^8
# 10 zeros for nupdates to be 4000
# 1000000000 is 2000 iterations and so
# 2000000000 is 4000 iterations.
logger.configure(folder=saver.data_dir)
model.learn(total_timesteps=int(25000000),
log_dir=saver.data_dir,
logger=logger)
model.save(saver.data_dir)
# # Testing mode with a trained weight
else:
model = PPO2.load(args.weight)
test_model(env, model, render=args.render)
def create_test_env(env_id,
n_envs=1,
is_atari=False,
stats_path=None,
norm_reward=False,
seed=0,
log_dir='',
should_render=True):
"""
Create environment for testing a trained agent
:param env_id: (str)
:param n_envs: (int) number of processes
:param is_atari: (bool)
:param stats_path: (str) path to folder containing saved running averaged
:param norm_reward: (bool) Whether to normalize rewards or not when using Vecnormalize
:param seed: (int) Seed for random number generator
:param log_dir: (str) Where to log rewards
:param should_render: (bool) For Pybullet env, display the GUI
:return: (gym.Env)
"""
# HACK to save logs
if log_dir is not None:
os.environ["OPENAI_LOG_FORMAT"] = 'csv'
os.environ["OPENAI_LOGDIR"] = os.path.abspath(log_dir)
os.makedirs(log_dir, exist_ok=True)
logger.configure()
# Create the environment and wrap it if necessary
if is_atari:
print("Using Atari wrapper")
env = make_atari_env(env_id, num_env=n_envs, seed=seed)
# Frame-stacking with 4 frames
env = VecFrameStack(env, n_stack=4)
elif n_envs > 1:
env = SubprocVecEnv(
[make_env(env_id, i, seed, log_dir) for i in range(n_envs)])
# Pybullet envs does not follow gym.render() interface
elif "Bullet" in env_id:
spec = gym.envs.registry.env_specs[env_id]
class_ = load(spec._entry_point)
# HACK: force SubprocVecEnv for Bullet env that does not
# have a render argument
use_subproc = 'renders' not in inspect.getfullargspec(
class_.__init__).args
# Create the env, with the original kwargs, and the new ones overriding them if needed
def _init():
# TODO: fix for pybullet locomotion envs
env = class_(**{**spec._kwargs}, renders=should_render)
env.seed(0)
if log_dir is not None:
env = Monitor(env,
os.path.join(log_dir, "0"),
allow_early_resets=True)
return env
if use_subproc:
env = SubprocVecEnv([make_env(env_id, 0, seed, log_dir)])
else:
env = DummyVecEnv([_init])
else:
env = DummyVecEnv([make_env(env_id, 0, seed, log_dir)])
# Load saved stats for normalizing input and rewards
if stats_path is not None:
print("Loading running average")
env = VecNormalize(env, training=False, norm_reward=norm_reward)
env.load_running_average(stats_path)
return env
def main(args):
logger.configure(config.LOGDIR)
if args.debug:
logger.set_level(config.DEBUG)
else:
logger.set_level(config.INFO)
# make environment
env = get_environment(args.env_name)(verbose=args.verbose,
manual=args.manual)
env.seed(args.seed)
total_rewards = {}
first_time = True
if args.recommend:
ppo_model = load_model(env, 'best_model.zip')
ppo_agent = Agent('best_model', ppo_model)
else:
ppo_agent = None
agents = []
# load the agents
if len(args.agents) != env.n_players:
raise Exception(
f'{len(args.agents)} players specified but this is a {env.n_players} player game!'
)
for i, agent in enumerate(args.agents):
if agent == 'human':
agent_obj = Agent('human')
elif agent == 'rules':
agent_obj = Agent('rules')
elif agent == 'json':
# Start mq server
context = zmq.Context()
socket = context.socket(zmq.REP)
socket.bind("tcp://*:5555")
logger.debug("zaq server start at 5555")
agent_obj = Agent('json')
elif agent == 'base':
base_model = load_model(env, 'base.zip')
agent_obj = Agent('base', base_model)
else:
ppo_model = load_model(env, f'{agent}.zip')
agent_obj = Agent(agent, ppo_model)
agents.append(agent_obj)
total_rewards[agent_obj.id] = 0
# play games
logger.info(f'\nPlaying {args.games} games...')
for game in range(args.games):
players = agents[:]
if args.randomise_players:
random.shuffle(players)
obs = env.reset()
done = False
for i, p in enumerate(players):
logger.debug(f'Player {i+1} = {p.name}')
while not done:
current_player = players[env.current_player_num]
env.render()
logger.debug(f'\nCurrent player name: {current_player.name}')
if args.recommend and current_player.name in [
'human', 'rules', 'json'
]:
# show recommendation from last loaded model
logger.debug(f'\nRecommendation by {ppo_agent.name}:')
action = ppo_agent.choose_action(env,
choose_best_action=True,
mask_invalid_actions=True)
if current_player.name == 'human':
action = input('\nPlease choose an action: ')
try:
# for int actions
action = int(action)
except:
# for MulitDiscrete action input as list TODO
action = eval(action)
if current_player.name == 'json':
if (not first_time):
game_state = {
"legal_action":
[i for i, o in enumerate(env.legal_actions) if o != 0],
"tableCard":
env.tableCard.id
}
socket.send_json(game_state)
action = socket.recv_json()
first_time = False
logger.debug(f'\nReceived {action}')
# action = input('\n JSON!!! Please choose an action: ')
try:
# for int actions
action = int(action)
except:
# for MulitDiscrete action input as list TODO
action = eval(action)
elif current_player.name == 'rules':
logger.debug(f'\n{current_player.name} model choices')
action = current_player.choose_action(
env, choose_best_action=False, mask_invalid_actions=True)
else:
logger.debug(f'\n{current_player.name} model choices')
action = current_player.choose_action(
env,
choose_best_action=args.best,
mask_invalid_actions=True)
obs, reward, done, _ = env.step(action)
for r, player in zip(reward, players):
total_rewards[player.id] += r
player.points += r
if args.cont:
input('Press any key to continue')
env.render()
logger.info(f"Played {game + 1} games: {total_rewards}")
if args.write_results:
write_results(players, game, args.games, env.turns_taken)
for p in players:
p.points = 0
env.close()
def rollouts_and_policy(
_seed: int,
env_name: str,
total_timesteps: int,
*,
log_dir: str = None,
num_vec: int = 8,
parallel: bool = False,
max_episode_steps: Optional[int] = None,
normalize: bool = True,
make_blank_policy_kwargs: dict = {},
reward_type: Optional[str] = None,
reward_path: Optional[str] = None,
rollout_save_interval: int = 0,
rollout_save_final: bool = False,
rollout_save_n_timesteps: Optional[int] = None,
rollout_save_n_episodes: Optional[int] = None,
policy_save_interval: int = -1,
policy_save_final: bool = True,
) -> None:
"""Trains an expert policy from scratch and saves the rollouts and policy.
At applicable training steps `step` (where step is either an integer or
"final"):
- Policies are saved to `{log_dir}/policies/{step}.pkl`.
- Rollouts are saved to `{log_dir}/rollouts/{step}.pkl`.
Args:
env_name: The gym.Env name. Loaded as VecEnv.
total_timesteps: Number of training timesteps in `model.learn()`.
log_dir: The root directory to save metrics and checkpoints to.
num_vec: Number of environments in VecEnv.
parallel: If True, then use DummyVecEnv. Otherwise use SubprocVecEnv.
max_episode_steps: If not None, then environments are wrapped by
TimeLimit so that they have at most `max_episode_steps` steps per
episode.
normalize: If True, then rescale observations and reward.
make_blank_policy_kwargs: Kwargs for `make_blank_policy`.
reward_type: If provided, then load the serialized reward of this type,
wrapping the environment in this reward. This is useful to test
whether a reward model transfers. For more information, see
`imitation.rewards.serialize.load_reward`.
reward_path: A specifier, such as a path to a file on disk, used by
reward_type to load the reward model. For more information, see
`imitation.rewards.serialize.load_reward`.
rollout_save_interval: The number of training updates in between
intermediate rollout saves. If the argument is nonpositive, then
don't save intermediate updates.
rollout_save_final: If True, then save rollouts right after training is
finished.
rollout_save_n_timesteps: The minimum number of timesteps saved in every
file. Could be more than `rollout_save_n_timesteps` because
trajectories are saved by episode rather than by transition.
Must set exactly one of `rollout_save_n_timesteps`
and `rollout_save_n_episodes`.
rollout_save_n_episodes: The number of episodes saved in every
file. Must set exactly one of `rollout_save_n_timesteps` and
`rollout_save_n_episodes`.
policy_save_interval: The number of training updates between saves. Has
the same semantics are `rollout_save_interval`.
policy_save_final: If True, then save the policy right after training is
finished.
"""
_validate_traj_generate_params(rollout_save_n_timesteps,
rollout_save_n_episodes)
with util.make_session():
tf.logging.set_verbosity(tf.logging.INFO)
sb_logger.configure(folder=osp.join(log_dir, 'rl'),
format_strs=['tensorboard', 'stdout'])
rollout_dir = osp.join(log_dir, "rollouts")
policy_dir = osp.join(log_dir, "policies")
os.makedirs(rollout_dir, exist_ok=True)
os.makedirs(policy_dir, exist_ok=True)
venv = util.make_vec_env(env_name,
num_vec,
seed=_seed,
parallel=parallel,
log_dir=log_dir,
max_episode_steps=max_episode_steps)
log_callbacks = []
with contextlib.ExitStack() as stack:
if reward_type is not None:
reward_fn_ctx = load_reward(reward_type, reward_path, venv)
reward_fn = stack.enter_context(reward_fn_ctx)
venv = RewardVecEnvWrapper(venv, reward_fn)
log_callbacks.append(venv.log_callback)
tf.logging.info(
f"Wrapped env in reward {reward_type} from {reward_path}.")
vec_normalize = None
if normalize:
venv = vec_normalize = VecNormalize(venv)
policy = util.init_rl(venv, verbose=1, **make_blank_policy_kwargs)
# Make callback to save intermediate artifacts during training.
step = 0
def callback(locals_: dict, _) -> bool:
nonlocal step
step += 1
policy = locals_['self']
# TODO(adam): make logging frequency configurable
for callback in log_callbacks:
callback(sb_logger)
if rollout_save_interval > 0 and step % rollout_save_interval == 0:
util.rollout.save(rollout_dir,
policy,
venv,
step,
n_timesteps=rollout_save_n_timesteps,
n_episodes=rollout_save_n_episodes)
if policy_save_interval > 0 and step % policy_save_interval == 0:
output_dir = os.path.join(policy_dir, f'{step:05d}')
serialize.save_stable_model(output_dir, policy,
vec_normalize)
return True # Continue training.
policy.learn(total_timesteps, callback=callback)
# Save final artifacts after training is complete.
if rollout_save_final:
util.rollout.save(rollout_dir,
policy,
venv,
"final",
n_timesteps=rollout_save_n_timesteps,
n_episodes=rollout_save_n_episodes)
if policy_save_final:
output_dir = os.path.join(policy_dir, "final")
serialize.save_stable_model(output_dir, policy, vec_normalize)
def main():
global model, best_model_path, last_model_path, sim_joy
mission = 'PushStonesEnv' # Change according to algorithm
env = gym.make(mission + '-v0').unwrapped
# Create log and model dir
dir = 'stable_bl/' + mission
# dir = 'stable_bl/PushMultipleStones'
os.makedirs(dir + '/model_dir/sac', exist_ok=True)
jobs = ['train', 'record', 'record-w/hm', 'BC_agent', 'play']
job = jobs[0]
name = 'PickUp_40_episodes'
pretrain = False
fillBuffer = False
if job == 'train':
# create new folder
try:
tests = os.listdir(dir + '/model_dir/sac')
indexes = []
for item in tests:
indexes.append(int(item.split('_')[1]))
if not bool(indexes):
k = 0
else:
k = max(indexes) + 1
except FileNotFoundError:
os.makedirs(dir + '/log_dir/sac')
k = 0
model_dir = os.getcwd() + '/' + dir + '/model_dir/sac/test_{}'.format(
str(k))
best_model_path = model_dir
last_model_path = model_dir
log_dir = dir + '/log_dir/sac/test_{}'.format(str(k))
logger.configure(folder=log_dir,
format_strs=['stdout', 'log', 'csv', 'tensorboard'])
num_timesteps = int(1e6)
policy_kwargs = dict(layers=[64, 64, 64])
# SAC - start learning from scratch
model = SAC(CnnPolicy,
env,
gamma=0.99,
learning_rate=1e-4,
buffer_size=50000,
learning_starts=1000,
train_freq=1,
batch_size=64,
tau=0.01,
ent_coef='auto',
target_update_interval=1,
gradient_steps=1,
target_entropy='auto',
action_noise=None,
random_exploration=0.0,
verbose=2,
tensorboard_log=log_dir,
_init_setup_model=True,
full_tensorboard_log=True,
seed=None,
n_cpu_tf_sess=None)
# Load best model and continue learning
# models = os.listdir(dir + '/model_dir/sac')
# models_rew = (model for model in models if 'rew' in model)
# ind, reward = [], []
# for model in models_rew:
# ind.append(model.split('_')[1])
# reward.append(model.split('_')[3])
# best_reward = max(reward)
# best_model_ind = reward.index(best_reward)
# k = ind[best_model_ind]
# model = SAC.load(dir + '/model_dir/sac/test_' + k + '_rew_' + best_reward, env=env,
# custom_objects=dict(learning_starts=0))
# Load last saved model and continue learning
# models = os.listdir(dir + '/model_dir/sac')
# models_time = (model for model in models if 'rew' not in model)
# ind, hour, min = [], [], []
# for model in models_time:
# ind.append(model.split('_')[1])
# hour.append(model.split('_')[3])
# min.append(model.split('_')[4])
# date = models_time[0].split('_')[2]
# latest_hour = max(hour)
# latest_hour_ind = [i for i, n in enumerate(hour) if n == latest_hour]
# latest_min = max(min[latest_hour_ind])
# latest_min_ind = min(latest_min)
# k = ind[latest_min_ind]
# model = SAC.load(dir + '/model_dir/sac/test_' + k + '_' + date + '_' + latest_hour[0] + '_' + latest_min + 'zip',
# env=env, custom_objects=dict(learning_starts=0))
# model = SAC.load(dir + '/model_dir/sac/test_0_11_16_2.zip',
# env=env, tensorboard_log=log_dir,
# custom_objects=dict(learning_starts=0)) #, learning_rate=2e-4,
# # train_freq=8, gradient_steps=4, target_update_interval=4))
# # # batch_size=32))
# pretrain
if pretrain:
# load dataset only once
# expert_dataset(name)
dataset = ExpertDataset(expert_path=(os.getcwd() + '/' + name +
'_dataset.npz'),
traj_limitation=-1)
model.pretrain(dataset, n_epochs=2000)
# fill replay buffer with Benny's recordings
if fillBuffer:
traj = expert_dataset(name)
for i in range(len(traj['obs']) - 1):
if traj['episode_starts'][i + 1]:
done = True
else:
done = False
obs = traj['obs'][i]
action = traj['actions'][i]
reward = traj['rewards'][i]
next_obs = traj['obs'][i + 1]
model.replay_buffer.add(obs, action, reward, next_obs,
float(done))
# Test the pre-trained model
# env = model.get_env()
# obs = env.reset()
#
# reward_sum = 0.0
# for _ in range(1000):
# action, _ = model.predict(obs)
# obs, reward, done, _ = env.step(action)
# reward_sum += reward
# if done:
# print(reward_sum)
# reward_sum = 0.0
# obs = env.reset()
#
# env.close()
# learn
model.learn(total_timesteps=num_timesteps, callback=save_fn)
# PPO1
# model = PPO1(Common_MlpPolicy, env, gamma=0.99, timesteps_per_actorbatch=256, clip_param=0.2, entcoeff=0.01,
# optim_epochs=4, optim_stepsize=1e-3, optim_batchsize=64, lam=0.95, adam_epsilon=1e-5,
# schedule='linear', verbose=0, tensorboard_log=None, _init_setup_model=True,
# policy_kwargs=None, full_tensorboard_log=False, seed=None, n_cpu_tf_sess=1)
# TRPO
# model = TRPO(MlpPolicy, env, timesteps_per_batch=4096, tensorboard_log=log_dir, verbose=1)
# model.learn(total_timesteps=500000)
# model.save(log_dir)
elif job == 'record':
mission = 'PushStonesHeatMapEnv'
env = gym.make(mission + '-v0').unwrapped
obs = []
actions = []
rewards = []
dones = []
episode_rewards = []
num_episodes = 30
listener = keyboard.Listener(on_press=on_press)
listener.start()
for episode in range(num_episodes):
ob = env.reset()
done = False
print('Episode number ', episode + 1)
episode_reward = 0
while not done:
act = "recording"
# act = sim_joy
# act = [0,1,0.5]
new_ob, reward, done, info = env.step(act)
# print(info['action'])
# print(ob)
if recorder_on:
obs.append(ob)
actions.append(info['action'])
rewards.append(reward)
dones.append(done)
episode_reward = episode_reward + reward
ob = new_ob
episode_rewards.append(episode_reward)
if info['reset reason'] == 'out of boarders' or info[
'reset reason'] == 'limit time steps':
episode -= 1
else:
print('saving data')
data_saver(obs, actions, rewards, dones, episode_rewards)
elif job == 'play':
# env = gym.make('PickUpEnv-v0')
model = SAC.load(dir + '/model_dir/sac/test_0_11_16_2.zip',
env=env,
custom_objects=dict(learning_starts=0)) ### ADD NUM
for _ in range(2):
obs = env.reset()
done = False
while not done:
action, _states = model.predict(obs)
obs, reward, done, info = env.step(action)
if policy == "dnn":
if algo == "dqn":
from stable_baselines.deepq.policies import MlpPolicy
else:
from stable_baselines.common.policies import MlpPolicy
policyFn = MlpPolicy
elif policy == "bnn":
if algo == "dqn":
from dqn_model import BnnPolicy
else:
from model import BnnPolicy
policyFn = BnnPolicy
log_dir = f"{algo}-{policy}-{tag}"
logger.configure(folder=log_dir)
env = gym.make("SlimeVolley-v0")
env.atari_mode = True
env.survival_bonus = True
env.__init__()
env.seed(seed)
eval_env = gym.make("SlimeVolley-v0")
eval_env.atari_mode = True
eval_env.__init__()
eval_env.seed(seed)
eval_callback = EvalCallback(eval_env,
best_model_save_path=log_dir,
log_path=log_dir,
eval_freq=eval_freq,
def train(
self,
seed: int,
communication_queue: Queue = None,
current_iteration: int = -1,
search_suffix: str = "1",
env_variables: EnvVariables = None,
random_search: bool = False,
):
self._set_global_seed(seed=seed)
env_kwargs_to_set = env_variables if env_variables else self.env_kwargs
self.logger.debug("env_variables: {}".format(env_kwargs_to_set.get_params_string()))
reward_threshold = get_reward_threshold(env_name=self.env_name)
best_model_save_path, tensorboard_log_dir = self._preprocess_storage_dirs()
if current_iteration != -1 and not self.continue_learning:
best_model_save_path = best_model_save_path + "_" + str(current_iteration)
self.logger.debug("best_model_save_path: {}".format(best_model_save_path))
if communication_queue or search_suffix != "1":
continue_learning_suffix = self.continue_learning_suffix + "_" + search_suffix
else:
continue_learning_suffix = self.continue_learning_suffix
os.environ["OPENAI_LOG_FORMAT"] = "log,csv"
if self.continue_learning:
os.environ["OPENAI_LOGDIR"] = best_model_save_path + "_" + continue_learning_suffix
else:
os.environ["OPENAI_LOGDIR"] = best_model_save_path
configure()
if self.algo_hyperparams:
self.logger.debug("Overriding file specified hyperparams with {}".format(eval(self.algo_hyperparams)))
hyperparams = eval(self.algo_hyperparams)
else:
hyperparams = load_hyperparams(algo_name=self.algo_name, env_name=self.env_name, model_suffix=self.model_suffix)
(normalize_kwargs, n_envs, n_timesteps, log_every, hyperparams,) = self._preprocess_hyperparams(
_hyperparams=hyperparams
)
if n_envs > 1 and self.algo_name == "ppo2":
# On most env, SubprocVecEnv does not help and is quite memory hungry
env = DummyVecEnv(
[
make_env_parallel(
sb_version=self.sb_version,
seed=seed,
rank=i,
env_name=self.env_name,
continue_learning=self.continue_learning,
log_dir=best_model_save_path,
env_kwargs=env_kwargs_to_set,
algo_name=self.algo_name,
continue_learning_suffix=continue_learning_suffix,
)
for i in range(n_envs)
]
)
if len(normalize_kwargs) > 0:
env = normalize_env(
env=env,
vectorize=False,
orig_log_dir=best_model_save_path,
continue_learning=self.continue_learning,
sb_version=self.sb_version,
normalize_kwargs=normalize_kwargs,
)
else:
env = make_custom_env(
seed=seed,
sb_version=self.sb_version,
env_kwargs=env_kwargs_to_set,
normalize_kwargs=normalize_kwargs,
continue_learning=self.continue_learning,
log_dir=best_model_save_path,
env_name=self.env_name,
algo_name=self.algo_name,
continue_learning_suffix=continue_learning_suffix,
)
if self.n_eval_episodes > DEFAULT_N_EVAL_EPISODES:
analysis_callback = self.build_callback(
algo_name=self.algo_name,
continue_learning=self.continue_learning,
call_every=log_every,
eval_callback=self.eval_callback,
_reward_threshold=reward_threshold,
eval_episodes=self.n_eval_episodes,
_eval_env=make_custom_env(
seed=seed,
continue_learning=self.continue_learning,
sb_version=self.sb_version,
env_kwargs=env_kwargs_to_set,
env_name=self.env_name,
log_dir=best_model_save_path,
algo_name=self.algo_name,
normalize_kwargs=normalize_kwargs,
evaluate=True,
evaluate_during_learning=True,
continue_learning_suffix=continue_learning_suffix,
),
original_env=make_custom_env(
seed=seed,
continue_learning=self.continue_learning,
sb_version=self.sb_version,
env_kwargs=self.env_kwargs,
env_name=self.env_name,
log_dir=best_model_save_path,
algo_name=self.algo_name,
normalize_kwargs=normalize_kwargs,
evaluate=True,
evaluate_during_learning=True,
),
env_name=self.env_name,
_best_model_save_path=best_model_save_path,
num_envs=n_envs,
total_timesteps=n_timesteps,
continue_learning_suffix=continue_learning_suffix,
communication_queue=communication_queue,
env_eval_callback=self.env_eval_callback,
save_replay_buffer=self.save_replay_buffer,
save_model=self.save_model,
random_search=random_search,
)
else:
analysis_callback = self.build_callback(
algo_name=self.algo_name,
continue_learning=self.continue_learning,
call_every=log_every,
eval_callback=self.eval_callback,
_reward_threshold=reward_threshold,
eval_episodes=self.n_eval_episodes,
env_name=self.env_name,
_best_model_save_path=best_model_save_path,
num_envs=n_envs,
continue_learning_suffix=continue_learning_suffix,
save_replay_buffer=self.save_replay_buffer,
save_model=self.save_model,
random_search=random_search,
)
if self.continue_learning:
model = self.create_model(
seed=seed,
algo_name=self.algo_name,
env=env,
tensorboard_log_dir=tensorboard_log_dir,
hyperparams=hyperparams,
best_model_save_path=best_model_save_path,
n_timesteps=n_timesteps,
continue_learning=True,
env_name=self.env_name,
model_to_load=self.model_to_load,
save_replay_buffer=self.save_replay_buffer,
)
else:
model = self.create_model(
seed=seed,
algo_name=self.algo_name,
env=env,
tensorboard_log_dir=tensorboard_log_dir,
hyperparams=hyperparams,
env_name=self.env_name,
n_timesteps=n_timesteps,
model_to_load=self.model_to_load,
save_replay_buffer=self.save_replay_buffer,
)
try:
callback_list = [analysis_callback]
# if len(normalize_kwargs) > 0 and not self.continue_learning:
# callback_list = [self._build_vec_normalize_callback(save_path=best_model_save_path,
# log_every=log_every), analysis_callback]
if self.show_progress_bar:
with ProgressBarManager(total_timesteps=n_timesteps, sb_version=self.sb_version) as progress_callback:
callback_list.append(progress_callback)
if self.continue_learning and self.log_to_tensorboard:
model.learn(
total_timesteps=n_timesteps,
callback=callback_list,
tb_log_name=self.tb_log_name + "_" + continue_learning_suffix,
)
else:
model.learn(
total_timesteps=n_timesteps, callback=callback_list, tb_log_name=self.tb_log_name,
)
else:
if self.continue_learning and self.log_to_tensorboard:
model.learn(
total_timesteps=n_timesteps,
callback=callback_list,
tb_log_name=self.tb_log_name + "_" + continue_learning_suffix,
)
else:
self.logger.debug("Model learn start...")
model.learn(
total_timesteps=n_timesteps, callback=callback_list, tb_log_name=self.tb_log_name,
)
self.logger.debug("Model learn end")
except KeyboardInterrupt:
pass
finally:
if len(normalize_kwargs) > 0 and not self.continue_learning:
# Important: save the running average, for testing the agent we need that normalization
model.get_vec_normalize_env().save(os.path.join(best_model_save_path, "vecnormalize.pkl"))
# Release resources
env.close()
def create_test_env(env_id,
n_envs=1,
is_atari=False,
stats_path=None,
seed=0,
log_dir='',
should_render=True,
hyperparams=None):
"""
Create environment for testing a trained agent
:param env_id: (str)
:param n_envs: (int) number of processes
:param is_atari: (bool)
:param stats_path: (str) path to folder containing saved running averaged
:param seed: (int) Seed for random number generator
:param log_dir: (str) Where to log rewards
:param should_render: (bool) For Pybullet env, display the GUI
:param env_wrapper: (type) A subclass of gym.Wrapper to wrap the original
env with
:param hyperparams: (dict) Additional hyperparams (ex: n_stack)
:return: (gym.Env)
"""
# HACK to save logs
if log_dir is not None:
os.environ["OPENAI_LOG_FORMAT"] = 'csv'
os.environ["OPENAI_LOGDIR"] = os.path.abspath(log_dir)
os.makedirs(log_dir, exist_ok=True)
logger.configure()
# Create the environment and wrap it if necessary
env_wrapper = get_wrapper_class(hyperparams)
if 'env_wrapper' in hyperparams.keys():
del hyperparams['env_wrapper']
if is_atari:
print("Using Atari wrapper")
env = make_atari_env(env_id, num_env=n_envs, seed=seed)
# Frame-stacking with 4 frames
env = VecFrameStack(env, n_stack=4)
elif n_envs > 1:
# start_method = 'spawn' for thread safe
env = SubprocVecEnv([
make_env(env_id, i, seed, log_dir, wrapper_class=env_wrapper)
for i in range(n_envs)
])
# Pybullet envs does not follow gym.render() interface
elif "Bullet" in env_id:
spec = gym.envs.registry.env_specs[env_id]
try:
class_ = load(spec.entry_point)
except AttributeError:
# Backward compatibility with gym
class_ = load(spec._entry_point)
# HACK: force SubprocVecEnv for Bullet env that does not
# have a render argument
render_name = None
use_subproc = 'renders' not in inspect.getfullargspec(
class_.__init__).args
if not use_subproc:
render_name = 'renders'
# Dev branch of pybullet
# use_subproc = use_subproc and 'render' not in inspect.getfullargspec(class_.__init__).args
# if not use_subproc and render_name is None:
# render_name = 'render'
# Create the env, with the original kwargs, and the new ones overriding them if needed
def _init():
# TODO: fix for pybullet locomotion envs
env = class_(**{**spec._kwargs}, **{render_name: should_render})
env.seed(0)
if log_dir is not None:
env = Monitor(env,
os.path.join(log_dir, "0"),
allow_early_resets=True)
return env
if use_subproc:
env = SubprocVecEnv([
make_env(env_id, 0, seed, log_dir, wrapper_class=env_wrapper)
])
else:
env = DummyVecEnv([_init])
else:
env = DummyVecEnv(
[make_env(env_id, 0, seed, log_dir, wrapper_class=env_wrapper)])
# Load saved stats for normalizing input and rewards
# And optionally stack frames
if stats_path is not None:
if hyperparams['normalize']:
print("Loading running average")
print("with params: {}".format(hyperparams['normalize_kwargs']))
env = VecNormalize(env,
training=False,
**hyperparams['normalize_kwargs'])
env.load_running_average(stats_path)
n_stack = hyperparams.get('frame_stack', 0)
if n_stack > 0:
print("Stacking {} frames".format(n_stack))
env = VecFrameStack(env, n_stack)
return env
os.makedirs(top_log_dir, exist_ok=True)
test_num = 1
for hyperparams in hyperparams_list:
hyperparam_log_dir = os.path.join(top_log_dir, hyper_file_name(hyperparams))
os.makedirs(hyperparam_log_dir, exist_ok=True)
print("Beginning test", test_num, "of", len(hyperparams_list))
begin_perm_time = datetime.now()
for i in range(5,10):
run_dir = os.path.join(hyperparam_log_dir, "run_" + str(i) + "_monitor_dir")
hyperparamfilename = os.path.join(run_dir, "hyperparams.txt")
if os.path.exists(hyperparamfilename):
continue
os.makedirs(run_dir, exist_ok=True)
checkpoint_dir = os.path.join(run_dir, "model_checkpoints")
os.makedirs(checkpoint_dir, exist_ok=True)
logger.configure(run_dir)
env = create_env(n_envs=n_envs, env_name=env_name, log_dir=run_dir)
model = RLAgent('MlpPolicy', env, verbose=0, **hyperparams).learn(total_timesteps=timesteps, callback=callback)
model.save(run_dir + "final_agent.pkl")
del model
del env
gc.collect()
hyperparamfile = open(hyperparamfilename, 'w')
hyperparamfile.write(str(hyperparams))
hyperparamfile.write("\nn_envs = {}\n".format(n_envs))
hyperparamfile.write("RLAgent = {}\n".format(RLAgent))
hyperparamfile.write("Env = {}\n".format(args.env))
hyperparamfile.close()
print("time remaining:", (datetime.now() - begin_perm_time) * (len(hyperparams_list) - test_num))
test_num += 1
lr = args.lr
kwargs = kwargs_map[args.agent]
kwargs['learning_rate'] = lr
# kwargs['max_grad_norm'] = args.max_grad_norm
# kwargs['kfac_clip'] = args.kfac_clip
# kwargs['vf_coef'] = args.vf_coef
# kwargs['ent_coef'] = args.ent_coef
# kwargs['n_steps'] = args.n_steps
start_time = datetime.now()
log_dir = os.path.join("training_logs", run_name)
checkpoint_dir = os.path.join(log_dir, "model_checkpoints")
os.makedirs(log_dir, exist_ok=True)
os.makedirs(checkpoint_dir, exist_ok=True)
logger.configure(log_dir)
env = create_env(n_envs=args.n_envs, env_name=args.env, log_dir=log_dir)
RLAgent = AGENTS_MAP[args.agent]
hyperparamfilename = os.path.join(log_dir, "hyperparams.txt")
hyperparamfile = open(hyperparamfilename, 'w')
hyperparamfile.write(str(kwargs))
hyperparamfile.write("\nn_envs = {}\n".format(args.n_envs))
hyperparamfile.write("RLAgent = {}\n".format(RLAgent))
hyperparamfile.write("Env = {}\n".format(args.env))
hyperparamfile.close()
model = RLAgent('MlpPolicy', env, verbose=1, **kwargs).learn(total_timesteps=total_timesteps, callback=callback)
model.save(log_dir + "final_agent.pkl")
# env.save("trained_agents/env_" + run_name)
print(kwargs)
#
def do_ppos(ppos_args, result, intermediate_data_dir, origin_param):
ppos_args.alg = "ppo_subspace"
logger.log(f"#######TRAIN: {ppos_args}")
this_run_dir = get_dir_path_for_this_run(ppos_args)
if os.path.exists(this_run_dir):
import shutil
shutil.rmtree(this_run_dir)
os.makedirs(this_run_dir)
log_dir = get_log_dir(this_run_dir)
save_dir = get_save_dir(this_run_dir)
full_param_traj_dir_path = get_full_params_dir(this_run_dir)
if os.path.exists(full_param_traj_dir_path):
import shutil
shutil.rmtree(full_param_traj_dir_path)
os.makedirs(full_param_traj_dir_path)
if os.path.exists(save_dir):
import shutil
shutil.rmtree(save_dir)
os.makedirs(save_dir)
run_info = {"full_param_traj_dir_path": full_param_traj_dir_path}
logger.configure(log_dir)
tic = time.time()
def make_env():
env_out = gym.make(ppos_args.env)
env_out.env.disableViewer = True
env_out.env.visualize = False
env_out = bench.Monitor(env_out,
logger.get_dir(),
allow_early_resets=True)
return env_out
env = DummyVecEnv([make_env])
if ppos_args.normalize:
env = VecNormalize(env)
set_global_seeds(ppos_args.seed)
policy = MlpMultPolicy
model = PPO2(policy=policy,
env=env,
n_steps=ppos_args.n_steps,
nminibatches=ppos_args.nminibatches,
lam=0.95,
gamma=0.99,
noptepochs=10,
ent_coef=0.0,
learning_rate=3e-4,
cliprange=0.2,
policy_kwargs={"num_comp": len(result["first_n_pcs"])},
pcs=result["first_n_pcs"],
origin_theta=origin_param)
model.tell_run_info(run_info)
eprews, optimization_path = model.learn(
total_timesteps=ppos_args.ppos_num_timesteps,
give_optimization_path=True)
toc = time.time()
logger.log(
f"####################################PPOS took {toc-tic} seconds")
moving_ave_rewards = get_moving_aves(eprews, 100)
return eprews, moving_ave_rewards, optimization_path
def create_test_env(env_id, n_envs=1, is_atari=False,
stats_path=None, seed=0,
log_dir='', should_render=True, hyperparams=None):
"""
Create environment for testing a trained agent
:param env_id: (str)
:param n_envs: (int) number of processes
:param is_atari: (bool)
:param stats_path: (str) path to folder containing saved running averaged
:param seed: (int) Seed for random number generator
:param log_dir: (str) Where to log rewards
:param should_render: (bool) For Pybullet env, display the GUI
:param env_wrapper: (type) A subclass of gym.Wrapper to wrap the original
env with
:param hyperparams: (dict) Additional hyperparams (ex: n_stack)
:return: (gym.Env)
"""
# HACK to save logs
if log_dir is not None:
os.environ["OPENAI_LOG_FORMAT"] = 'csv'
os.environ["OPENAI_LOGDIR"] = os.path.abspath(log_dir)
os.makedirs(log_dir, exist_ok=True)
logger.configure()
if hyperparams is None:
hyperparams = {}
# Create the environment and wrap it if necessary
env_wrapper = get_wrapper_class(hyperparams)
if 'env_wrapper' in hyperparams.keys():
del hyperparams['env_wrapper']
if is_atari:
print("Using Atari wrapper")
env = make_atari_env(env_id, num_env=n_envs, seed=seed)
# Frame-stacking with 4 frames
env = VecFrameStack(env, n_stack=4)
elif n_envs > 1:
# start_method = 'spawn' for thread safe
env = SubprocVecEnv([make_env(env_id, i, seed, log_dir, wrapper_class=env_wrapper) for i in range(n_envs)])
# Pybullet envs does not follow gym.render() interface
elif "Bullet" in env_id:
# HACK: force SubprocVecEnv for Bullet env
env = SubprocVecEnv([make_env(env_id, 0, seed, log_dir, wrapper_class=env_wrapper)])
else:
env = DummyVecEnv([make_env(env_id, 0, seed, log_dir, wrapper_class=env_wrapper)])
# Load saved stats for normalizing input and rewards
# And optionally stack frames
if stats_path is not None:
if hyperparams['normalize']:
print("Loading running average")
print("with params: {}".format(hyperparams['normalize_kwargs']))
env = VecNormalize(env, training=False, **hyperparams['normalize_kwargs'])
if os.path.exists(os.path.join(stats_path, 'vecnormalize.pkl')):
env = VecNormalize.load(os.path.join(stats_path, 'vecnormalize.pkl'), env)
# Deactivate training and reward normalization
env.training = False
env.norm_reward = False
else:
# Legacy:
env.load_running_average(stats_path)
n_stack = hyperparams.get('frame_stack', 0)
if n_stack > 0:
print("Stacking {} frames".format(n_stack))
env = VecFrameStack(env, n_stack)
return env
def train(env_id, algo, num_timesteps, seed, sgd_steps, t_pi, t_c, lam, log,
expert_path, pretrain, pretrain_epochs, mdpo_update_steps,
num_trajectories, expert_model, exploration_bonus, bonus_coef,
random_action_len, is_action_features, dir_name, neural, lipschitz,
args):
"""
Train TRPO model for the mujoco environment, for testing purposes
:param env_id: (str) Environment ID
:param num_timesteps: (int) The total number of samples
:param seed: (int) The initial seed for training
"""
with tf_util.single_threaded_session():
# from mpi4py import MPI
# rank = MPI.COMM_WORLD.Get_rank()
rank = 0
env_name = env_id[:-3].lower()
log_dir = './experiments/' + env_name + '/' + str(algo).lower() + '/'\
+ 'tpi' + str(t_pi) + '_tc' + str(t_c) + '_lam' + str(lam)
log_dir += '_' + dir_name + '/'
log_name = str(algo) + '_updateSteps' + str(mdpo_update_steps)
# log_name += '_randLen' + str(random_action_len)
if exploration_bonus:
log_name += '_exploration' + str(bonus_coef)
if pretrain:
log_name += '_pretrain' + str(pretrain_epochs)
if not is_action_features:
log_name += "_states_only"
log_name += '_s' + str(seed)
log_path = log_dir + log_name
expert_path = './experts/' + expert_path
num_timesteps = int(num_timesteps)
args = args.__dict__
dir_path = os.getcwd() + log_dir[1:]
if not os.path.exists(dir_path):
os.makedirs(dir_path)
with open(os.getcwd() + log_dir[1:] + 'args.txt', 'w') as file:
file.write("Experiment Arguments:")
for key, val in args.items():
print(key, ": ", val, file=file)
if log:
if rank == 0:
logger.configure(log_path)
else:
logger.configure(log_path, format_strs=[])
logger.set_level(logger.DISABLED)
else:
if rank == 0:
logger.configure()
else:
logger.configure(format_strs=[])
logger.set_level(logger.DISABLED)
# workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
# env = make_mujoco_env(env_id, workerseed)
def make_env():
# env_out = gym.make(env_id, reset_noise_scale=1.0)
env_out = gym.make(env_id)
env_out = bench.Monitor(env_out,
logger.get_dir(),
allow_early_resets=True)
env_out.seed(seed)
env_out = wrap_mujoco(env_out, random_action_len=random_action_len)
return env_out
#
env = DummyVecEnv([make_env])
# env = VecNormalize(env)
if algo == 'Train':
train = True
else:
train = False
if algo == 'Evaluate':
eval = True
else:
eval = False
if train:
from stable_baselines import SAC
env = VecNormalize(env, norm_reward=False, norm_obs=False)
if num_timesteps > 0:
model = SAC('MlpPolicy',
env_id,
verbose=1,
buffer_size=1000000,
batch_size=256,
ent_coef='auto',
train_freq=1,
tau=0.01,
gradient_steps=1,
learning_starts=10000)
else:
model = SAC.load(expert_model, env)
generate_expert_traj(model,
expert_path,
n_timesteps=num_timesteps,
n_episodes=num_trajectories)
if num_timesteps > 0:
model.save('sac_' + env_name + '_' + str(num_timesteps))
elif eval:
from stable_baselines import SAC
env = VecNormalize(env, norm_reward=False, norm_obs=False)
model = SAC.load(expert_model, env)
generate_expert_traj(model,
expert_path,
n_timesteps=num_timesteps,
n_episodes=10,
evaluate=True)
else:
expert_path = expert_path + '.npz'
dataset = ExpertDataset(expert_path=expert_path,
traj_limitation=10,
verbose=1)
if algo == 'MDAL':
model = MDAL_MDPO_OFF('MlpPolicy',
env,
dataset,
verbose=1,
tensorboard_log="./experiments/" +
env_name + "/mdal/",
seed=seed,
buffer_size=1000000,
ent_coef=0.0,
learning_starts=10000,
batch_size=256,
tau=0.01,
gamma=0.99,
gradient_steps=sgd_steps,
mdpo_update_steps=mdpo_update_steps,
lam=0.0,
train_freq=1,
d_step=10,
tsallis_q=1,
reparameterize=True,
t_pi=t_pi,
t_c=t_c,
exploration_bonus=exploration_bonus,
bonus_coef=bonus_coef,
is_action_features=is_action_features,
neural=neural,
lipschitz=lipschitz)
elif algo == 'MDAL_ON_POLICY':
model = MDAL_MDPO_ON('MlpPolicy',
env,
dataset,
verbose=1,
timesteps_per_batch=2048,
tensorboard_log="./experiments/" +
env_name + "/mdal_mdpo_on/",
seed=seed,
max_kl=0.01,
cg_iters=10,
cg_damping=0.1,
entcoeff=0.0,
adversary_entcoeff=0.001,
gamma=0.99,
lam=0.95,
vf_iters=5,
vf_stepsize=1e-3,
sgd_steps=sgd_steps,
klcoeff=1.0,
method="multistep-SGD",
tsallis_q=1.0,
t_pi=t_pi,
t_c=t_c,
exploration_bonus=exploration_bonus,
bonus_coef=bonus_coef,
is_action_features=is_action_features,
neural=neural)
elif algo == 'MDAL_TRPO':
model = MDAL_TRPO('MlpPolicy',
env,
dataset,
verbose=1,
tensorboard_log="./experiments/" + env_name +
"/mdal_trpo/",
seed=seed,
gamma=0.99,
g_step=3,
d_step=5,
sgd_steps=1,
d_stepsize=9e-5,
entcoeff=0.0,
adversary_entcoeff=0.001,
max_kl=t_pi,
t_pi=t_pi,
t_c=t_c,
exploration_bonus=exploration_bonus,
bonus_coef=bonus_coef,
is_action_features=is_action_features,
neural=neural,
lam=0.98,
timesteps_per_batch=2000,
lipschitz=lipschitz)
elif algo == 'GAIL':
from mpi4py import MPI
from stable_baselines import GAIL
model = GAIL('MlpPolicy',
env,
dataset,
verbose=1,
tensorboard_log="./experiments/" + env_name +
"/gail/",
seed=seed,
entcoeff=0.0,
adversary_entcoeff=0.001,
lipschitz=lipschitz)
elif algo == 'GAIL_MDPO_OFF':
# from mpi4py import MPI
from stable_baselines import GAIL_MDPO_OFF
model = GAIL_MDPO_OFF('MlpPolicy',
env,
dataset,
verbose=1,
tensorboard_log="./experiments/" +
env_name + "/gail_mdpo_off/",
seed=seed,
ent_coef=0.0,
adversary_entcoeff=0.001,
buffer_size=1000000,
learning_starts=10000,
batch_size=256,
tau=0.01,
gamma=0.99,
gradient_steps=sgd_steps,
mdpo_update_steps=mdpo_update_steps,
lam=0.0,
train_freq=1,
tsallis_q=1,
reparameterize=True,
t_pi=t_pi,
t_c=t_c,
exploration_bonus=exploration_bonus,
bonus_coef=bonus_coef,
is_action_features=is_action_features,
lipschitz=lipschitz)
else:
raise ValueError("Not a valid algorithm.")
if pretrain:
model.pretrain(dataset, n_epochs=pretrain_epochs)
model.learn(total_timesteps=num_timesteps, tb_log_name=log_name)
env.close()
import sys
sys.path.append('/home/frcvision1/Final/My_Environments/Carla-0.9.4')
sys.path.append('/home/frcvision1/Final/learning-to-drive-in-a-day-carla-0.9')
from stable_baselines.common.vec_env import DummyVecEnv
from vae.controller import VAEController
from stable_baselines import logger
import os
from ppo_with_vae import PPOWithVAE
from stable_baselines.ppo2.ppo2 import PPO2
from stable_baselines.common.policies import MlpPolicy
import numpy as np
vae = VAEController()
PATH_MODEL_VAE = "vae.json"
logger.configure(folder='/tmp/ppo_carla2/')
PATH_MODEL_PPO2 = "carla_ppo2_with_vae_500_2mil"
def make_carla_env():
"""Import the package for carla Env, this packge calls the __init__ that registers the environment.Did this just to
be consistent with gym"""
sys.path.append('/home/frcvision1/Final/My_Environments/Carla_new')
from env3 import CarlaEnv
env = CarlaEnv()
env = DummyVecEnv([lambda: env])
return env
env = make_carla_env()
def launch(env,
logdir,
n_epochs,
num_cpu,
seed,
replay_strategy,
policy_save_interval,
clip_return,
override_params=None,
save_policies=True):
"""
launch training with mpi
:param env: (str) environment ID
:param logdir: (str) the log directory
:param n_epochs: (int) the number of training epochs
:param num_cpu: (int) the number of CPUs to run on
:param seed: (int) the initial random seed
:param replay_strategy: (str) the type of replay strategy ('future' or 'none')
:param policy_save_interval: (int) the interval with which policy pickles are saved.
If set to 0, only the best and latest policy will be pickled.
:param clip_return: (float): clip returns to be in [-clip_return, clip_return]
:param override_params: (dict) override any parameter for training
:param save_policies: (bool) whether or not to save the policies
"""
if override_params is None:
override_params = {}
# Fork for multi-CPU MPI implementation.
if num_cpu > 1:
try:
whoami = mpi_fork(num_cpu, ['--bind-to', 'core'])
except CalledProcessError:
# fancy version of mpi call failed, try simple version
whoami = mpi_fork(num_cpu)
if whoami == 'parent':
sys.exit(0)
tf_util.single_threaded_session().__enter__()
rank = MPI.COMM_WORLD.Get_rank()
# Configure logging
if rank == 0:
if logdir or logger.get_dir() is None:
logger.configure(folder=logdir)
else:
logger.configure()
logdir = logger.get_dir()
assert logdir is not None
os.makedirs(logdir, exist_ok=True)
# Seed everything.
rank_seed = seed + 1000000 * rank
set_global_seeds(rank_seed)
# Prepare params.
params = config.DEFAULT_PARAMS
params['env_name'] = env
params['replay_strategy'] = replay_strategy
if env in config.DEFAULT_ENV_PARAMS:
params.update(
config.DEFAULT_ENV_PARAMS[env]) # merge env-specific parameters in
params.update(
**override_params) # makes it possible to override any parameter
with open(os.path.join(logger.get_dir(), 'params.json'),
'w') as file_handler:
json.dump(params, file_handler)
params = config.prepare_params(params)
config.log_params(params, logger_input=logger)
if num_cpu == 1:
logger.warn()
logger.warn('*** Warning ***')
logger.warn(
'You are running HER with just a single MPI worker. This will work, but the '
+
'experiments that we report in Plappert et al. (2018, https://arxiv.org/abs/1802.09464) '
+
'were obtained with --num_cpu 19. This makes a significant difference and if you '
+
'are looking to reproduce those results, be aware of this. Please also refer to '
+
'https://github.com/openai/stable_baselines/issues/314 for further details.'
)
logger.warn('****************')
logger.warn()
dims = config.configure_dims(params)
policy = config.configure_ddpg(dims=dims,
params=params,
clip_return=clip_return)
rollout_params = {
'exploit': False,
'use_target_net': False,
# 'use_demo_states': True,
'compute_q': False,
'time_horizon': params['time_horizon'],
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
# 'use_demo_states': False,
'compute_q': True,
'time_horizon': params['time_horizon'],
}
for name in [
'time_horizon', 'rollout_batch_size', 'gamma', 'noise_eps',
'random_eps'
]:
rollout_params[name] = params[name]
eval_params[name] = params[name]
rollout_worker = RolloutWorker(params['make_env'], policy, dims, logger,
**rollout_params)
rollout_worker.seed(rank_seed)
evaluator = RolloutWorker(params['make_env'], policy, dims, logger,
**eval_params)
evaluator.seed(rank_seed)
train(policy=policy,
rollout_worker=rollout_worker,
evaluator=evaluator,
n_epochs=n_epochs,
n_test_rollouts=params['n_test_rollouts'],
n_cycles=params['n_cycles'],
n_batches=params['n_batches'],
policy_save_interval=policy_save_interval,
save_policies=save_policies)
parser.add_argument('--seed', help='RNG seed', type=int, default=0)
parser.add_argument('--critic-l2-reg', type=float, default=1e-2)
parser.add_argument('--batch-size', type=int, default=64) # per MPI worker
parser.add_argument('--actor-lr', type=float, default=1e-4)
parser.add_argument('--critic-lr', type=float, default=1e-3)
boolean_flag(parser, 'enable-popart', default=False)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--reward-scale', type=float, default=1.)
parser.add_argument('--clip-norm', type=float, default=None)
parser.add_argument('--nb-train-steps', type=int,
default=50) # per epoch cycle and MPI worker
parser.add_argument('--nb-eval-steps', type=int,
default=100) # per epoch cycle and MPI worker
parser.add_argument('--nb-rollout-steps', type=int,
default=100) # per epoch cycle and MPI worker
# choices are adaptive-param_xx, ou_xx, normal_xx, none
parser.add_argument('--noise-type', type=str, default='adaptive-param_0.2')
parser.add_argument('--num-timesteps', type=int, default=int(1e6))
boolean_flag(parser, 'evaluation', default=False)
args = parser.parse_args()
dict_args = vars(args)
return dict_args
if __name__ == '__main__':
args = parse_args()
if MPI.COMM_WORLD.Get_rank() == 0:
logger.configure()
# Run actual script.
run(**args)
def main():
"""
Runs the test
"""
parser = mujoco_arg_parser()
parser.add_argument(
'--model-path',
default="/cvgl2/u/surajn/workspace/saved_models/sawyerlift_ppo2/model")
parser.add_argument('--images', default=False)
args = parser.parse_args()
logger.configure()
if not args.play:
model, env = train(args.env,
num_timesteps=args.num_timesteps,
seed=args.seed,
model_path=args.model_path,
images=args.images)
if args.play:
def make_env():
env_out = GymWrapper(
suite.make(
"SawyerLift",
use_camera_obs=False, # do not use pixel observations
has_offscreen_renderer=
False, # not needed since not using pixel obs
has_renderer=True, # make sure we can render to the screen
reward_shaping=True, # use dense rewards
control_freq=
10, # control should happen fast enough so that simulation looks smooth
))
env_out.reward_range = None
env_out.metadata = None
env_out.spec = None
env_out = bench.Monitor(env_out,
logger.get_dir(),
allow_early_resets=True)
return env_out
#env = make_env()
env = DummyVecEnv([make_env])
env = VecNormalize(env)
policy = MlpPolicy
#model = PPO1(MlpPolicy, env, timesteps_per_actorbatch=2048, clip_param=0.2, entcoeff=0.0, optim_epochs=10,
# optim_stepsize=3e-4, optim_batchsize=64, gamma=0.99, lam=0.95, schedule='linear', verbose=1)
model = TRPO(MlpPolicy,
env,
timesteps_per_batch=1024,
max_kl=0.01,
cg_iters=10,
cg_damping=0.1,
entcoeff=0.0,
gamma=0.99,
lam=0.98,
vf_iters=5,
vf_stepsize=1e-3)
model.load(args.model_path)
logger.log("Running trained model")
obs = np.zeros((env.num_envs, ) + env.observation_space.shape)
obs[:] = env.reset()
while True:
env.render()
actions = model.step(obs)[0]
obs[:] = env.step(actions)[0]
