def main(policy_file, seed, n_test_rollouts, render):
set_global_seeds(seed)
# Load policy.
with open(policy_file, 'rb') as f:
policy = pickle.load(f)
env_name = policy.info['env_name']
# Prepare params.
params = config.DEFAULT_PARAMS
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]) # merge env-specific parameters in
params['env_name'] = env_name
params = config.prepare_params(params)
config.log_params(params, logger=logger)
dims = config.configure_dims(params)
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'compute_Q': True,
'rollout_batch_size': 1,
'render': bool(render),
}
for name in ['T', 'gamma', 'noise_eps', 'random_eps']:
eval_params[name] = params[name]
evaluator = RolloutWorker(params['make_env'], policy, dims, logger, **eval_params)
evaluator.seed(seed)
# Run evaluation.
evaluator.clear_history()
for _ in range(n_test_rollouts):
evaluator.generate_rollouts()
# record logs
for key, val in evaluator.logs('test'):
logger.record_tabular(key, np.mean(val))
logger.dump_tabular()
def launch(env_name,
logdir,
n_epochs,
num_cpu,
seed,
replay_strategy,
policy_save_interval,
clip_return,
override_params={},
save_policies=True):
# Fork for multi-CPU MPI implementation.
if num_cpu > 1:
whoami = mpi_fork(num_cpu)
if whoami == 'parent':
sys.exit(0)
import baselines.common.tf_util as U
U.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(dir=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_name
params['replay_strategy'] = replay_strategy
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]
) # 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 f:
json.dump(params, f)
params = config.prepare_params(params)
config.log_params(params, logger=logger)
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,
'T': params['T'],
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
'T': params['T'],
}
for name in [
'T', '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(logdir=logdir,
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)
def launch(
env_name, logdir, n_epochs, num_cpu, seed, replay_strategy, policy_save_interval, clip_return,
override_params={}, save_policies=True
):
# Fork for multi-CPU MPI implementation.
if num_cpu > 1:
whoami = mpi_fork(num_cpu)
if whoami == 'parent':
sys.exit(0)
import baselines.common.tf_util as U
U.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(dir=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_name
params['replay_strategy'] = replay_strategy
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]) # 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 f:
json.dump(params, f)
params = config.prepare_params(params)
config.log_params(params, logger=logger)
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,
'T': params['T'],
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
'T': params['T'],
}
for name in ['T', '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(
logdir=logdir, 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)
def launch(
env_name, logdir, n_epochs, num_cpu, seed, replay_strategy, policy_save_interval, clip_return,
override_params={}, save_policies=True
):
# Fork for multi-CPU MPI implementation.
if num_cpu > 1:
whoami = mpi_fork(num_cpu)
if whoami == 'parent':
sys.exit(0)
import baselines.common.tf_util as U
U.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(dir=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_name
params['replay_strategy'] = replay_strategy
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]) # 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 f:
json.dump(params, f)
params = config.prepare_params(params)
config.log_params(params, logger=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/baselines/issues/314 for further details.')
logger.warn('****************')
logger.warn()
# Added the following line
dims = config.configure_dims(params)
params['replay_k'] = config.DEFAULT_PARAMS['replay_k']
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,
'T': params['T'],
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
'T': params['T'],
}
for name in ['T', '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(
logdir=logdir, 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)
def launch(env_name,
n_epochs,
num_cpu,
seed,
replay_strategy,
policy_save_interval,
clip_return,
binding,
logging,
version,
n_cycles,
note,
override_params={},
save_policies=True):
# Fork for multi-CPU MPI implementation.
if num_cpu > 1:
whoami = mpi_fork(num_cpu, binding)
if whoami == 'parent':
sys.exit(0)
import baselines.common.tf_util as U
U.single_threaded_session().__enter__()
rank = MPI.COMM_WORLD.Get_rank()
# Configure logging
if logging:
logdir = 'logs/' + str(env_name) + '-replay_strategy' + str(
replay_strategy) + '-n_epochs' + str(n_epochs) + '-num_cpu' + str(
num_cpu) + '-seed' + str(seed) + '-n_cycles' + str(
n_cycles) + '-version' + str(
version) + '-T-' + datetime.datetime.now().strftime(
"%Y-%m-%d-%H-%M-%S")
else:
logdir = osp.join(
tempfile.gettempdir(),
datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M-%S-%f"))
if rank == 0:
if logdir or logger.get_dir() is None:
logger.configure(dir=logdir)
else:
logger.configure() # use temp folder for other rank
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_name
params['replay_strategy'] = replay_strategy
params['binding'] = binding
params['max_timesteps'] = n_epochs * params['n_cycles'] * params[
'n_batches'] * num_cpu
params['version'] = version
params['n_cycles'] = n_cycles
params['num_cpu'] = num_cpu
params['note'] = note or params['note']
if note:
with open('params/' + env_name + '/' + note + '.json', 'r') as file:
override_params = json.loads(file.read())
params.update(**override_params)
if params['load_weight']:
if type(params['load_weight']) is list:
params['load_weight'] = params['load_weight'][seed]
base = os.path.splitext(params['load_weight'])[0]
policy_weight_file = open(base + '_weight.pkl', 'rb')
pretrain_weights = pickle.load(policy_weight_file)
policy_weight_file.close()
else:
pretrain_weights = None
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]
) # merge env-specific parameters in
with open(os.path.join(logger.get_dir(), 'params.json'), 'w') as f:
json.dump(params, f)
params = config.prepare_params(params)
config.log_params(params, logger=logger)
dims = config.configure_dims(params)
policy = config.configure_ddpg(dims=dims,
params=params,
pretrain_weights=pretrain_weights,
clip_return=clip_return)
render = False
if params['collect_video']:
render = 'rgb_array'
rollout_params = {
'exploit': False,
'use_target_net': False,
'use_demo_states': True,
'compute_Q': False,
'T': params['T'],
'render': render,
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
'T': params['T'],
}
for name in [
'T', '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(logdir=logdir,
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,
num_cpu=num_cpu,
collect_data=params['collect_data'],
collect_video=params['collect_video'],
goal_generation=params['goal_generation'],
num_skills=params['num_skills'],
use_skill_n=params['use_skill_n'],
batch_size=params['_batch_size'],
mi_r_scale=params['mi_r_scale'],
mi_end_epoch=params['mi_end_epoch'],
sk_r_scale=params['sk_r_scale'],
no_train_mi=params['no_train_mi'])
def run_task(v):
random.seed(v['seed'])
np.random.seed(v['seed'])
num_cpu = 1
if num_cpu > 1:
try:
whoami = mpi_fork(num_cpu, ['--bind-to', 'core'])
print("fancy call succeeded")
except CalledProcessError:
print("fancy version of mpi call failed, try simple version")
whoami = mpi_fork(num_cpu)
if whoami == 'parent':
sys.exit(0)
import baselines.common.tf_util as U
U.single_threaded_session().__enter__()
# Configure logging
rank = MPI.COMM_WORLD.Get_rank()
logdir = ''
if rank == 0:
if logdir or logger_b.get_dir() is None:
logger_b.configure(dir=logdir)
else:
logger_b.configure()
logdir = logger_b.get_dir()
assert logdir is not None
os.makedirs(logdir, exist_ok=True)
# Seed everything.
rank_seed = v['seed'] + 1000000 * rank
set_global_seeds(rank_seed)
def make_env():
return PnPEnv()
env = make_env()
test_env = make_env()
env.reset()
# for _ in range(1000):
# env.render()
# import pdb; pdb.set_trace()
# env.step(env.action_space.sample())
params = config.DEFAULT_PARAMS
params['action_l2'] = v['action_l2']
params['max_u'] = v['max_u']
params['gamma'] = v['discount']
params['env_name'] = 'FetchReach-v0'
params['replay_strategy'] = v['replay_strategy']
params['lr'] = v['lr']
params['layers'] = v['layers']
params['hidden'] = v['hidden']
params['n_cycles'] = v['n_cycles'] # cycles per epoch
params['n_batches'] = v['n_batches'] # training batches per cycle
params['batch_size'] = v[
'batch_size'] # per mpi thread, measured in transitions and reduced to even multiple of chunk_length.
params['n_test_rollouts'] = v[
'n_test_rollouts'] # changed from 10 to 3 # number of test rollouts per epoch, each consists of rollout_batch_size rollouts
# exploration
params['random_eps'] = 0.3 # percentage of time a random action is taken
params['noise_eps'] = v['action_noise']
params['goal_weight'] = v['goal_weight']
params['scope'] = 'ddpg3'
params['sample_expert'] = v['sample_expert']
params['expert_batch_size'] = v['expert_batch_size']
params['bc_loss'] = v['bc_loss']
params['anneal_bc'] = v['anneal_bc']
params['gail_weight'] = v['gail_weight']
params['terminate_bootstrapping'] = v['terminate_bootstrapping']
params['mask_q'] = int(v['mode'] == 'pure_bc')
params['two_qs'] = v['two_qs']
params['anneal_discriminator'] = v['anneal_discriminator']
params['two_rs'] = v['two_qs'] or v['anneal_discriminator']
params['with_termination'] = v['rollout_terminate']
if 'clip_dis' in v and v['clip_dis']:
params['dis_bound'] = v['clip_dis']
with open(os.path.join(logger_b.get_dir(), 'params.json'), 'w') as f:
json.dump(params, f)
params['T'] = v['horizon']
params['to_goal'] = v['to_goal']
params = config.prepare_params(params)
params['make_env'] = make_env
config.log_params(params, logger=logger_b)
dims = config.configure_dims(params)
# prepare GAIL
if v['use_s_p']:
discriminator = GAIL(dims['o'] + dims['o'] +
dims['g'] if not v['only_s'] else dims['o'] +
dims['g'],
dims['o'],
dims['o'],
dims['g'],
0.,
gail_loss=v['gail_reward'],
use_s_p=True,
only_s=v['only_s'])
else:
discriminator = GAIL(dims['o'] + dims['u'] +
dims['g'] if not v['only_s'] else dims['o'] +
dims['g'],
dims['o'],
dims['u'],
dims['g'],
0.,
gail_loss=v['gail_reward'],
only_s=v['only_s'])
params['discriminator'] = discriminator
# configure replay buffer for expert buffer
params_expert = {
k: params[k]
for k in [
'make_env', 'replay_k', 'discriminator', 'gail_weight', 'two_rs',
'with_termination'
]
}
params_expert[
'replay_strategy'] = 'future' if v['relabel_expert'] else 'none'
params_policy_buffer = {
k: params[k]
for k in [
'make_env', 'replay_k', 'discriminator', 'gail_weight', 'two_rs',
'with_termination'
]
}
params_policy_buffer['replay_strategy'] = 'future'
params_empty = {
k: params[k]
for k in [
'make_env', 'replay_k', 'discriminator', 'gail_weight',
'replay_strategy'
]
}
policy = config.configure_ddpg(dims=dims,
params=params,
clip_return=v['clip_return'],
reuse=tf.AUTO_REUSE,
env=env,
to_goal=v['to_goal'])
rollout_params = {
'exploit': False,
'use_target_net': False,
'use_demo_states': True,
'compute_Q': True,
'T': params['T'],
'weight': v['goal_weight'],
'rollout_terminate': v['rollout_terminate'],
'to_goal': v['to_goal']
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
'T': params['T'],
'weight': v['goal_weight'],
'rollout_terminate': v['rollout_terminate'],
'to_goal': v['to_goal']
}
for name in [
'T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps'
]:
rollout_params[name] = params[name]
eval_params[name] = params[name]
rollout_worker = RolloutWorker([env], policy, dims, logger_b,
**rollout_params)
# rollout_worker.seed(rank_seed)
evaluator = RolloutWorker([env], policy, dims, logger_b, **eval_params)
# evaluator.seed(rank_seed)
n_traj = v['n_evaluation_traj']
logger.log("Initializing report and plot_policy_reward...")
log_dir = logger.get_snapshot_dir()
inner_log_dir = osp.join(log_dir, 'inner_iters')
report = HTMLReport(osp.join(log_dir, 'report.html'), images_per_row=3)
report.add_header("{}".format(EXPERIMENT_TYPE))
report.add_text(format_dict(v))
logger.log("Starting the outer iterations")
logger.log("Generating heat map")
def evaluate_pnp(env, policy, n_rollouts=100):
goal_reached = []
distance_to_goal = []
for i in range(n_rollouts):
traj = rollout(env,
policy,
max_path_length=v['horizon'],
using_gym=True)
goal_reached.append(np.max(traj['env_infos']['goal_reached']))
distance_to_goal.append(np.min(traj['env_infos']['distance']))
return np.mean(goal_reached), np.mean(distance_to_goal)
from sandbox.experiments.goals.pick_n_place.pnp_expert import PnPExpert
expert_policy = PnPExpert(env)
expert_params = {
'exploit': not v['noisy_expert'],
'use_target_net': False,
'use_demo_states': False,
'compute_Q': False,
'T': params['T'],
'weight': v['goal_weight'],
'rollout_terminate': v['rollout_terminate'],
'to_goal': v['to_goal']
}
for name in [
'T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps'
]:
expert_params[name] = params[name]
expert_params['noise_eps'] = v['expert_noise']
expert_params['random_eps'] = v['expert_eps']
expert_worker = RolloutWorker([env], expert_policy, dims, logger_b,
**expert_params)
input_shapes = dims_to_shapes(dims)
expert_sample_transitions = config.configure_her(params_expert)
buffer_shapes = {
key:
(v['horizon'] if key != 'o' else v['horizon'] + 1, *input_shapes[key])
for key, val in input_shapes.items()
}
buffer_shapes['g'] = (buffer_shapes['g'][0],
3 if not v['full_space_as_goal'] else 6)
buffer_shapes['ag'] = (v['horizon'] + 1,
3 if not v['full_space_as_goal'] else 6)
buffer_shapes['successes'] = (v['horizon'], )
expert_buffer = ReplayBuffer(buffer_shapes, int(1e6), v['horizon'],
expert_sample_transitions)
policy.expert_buffer = expert_buffer
sample_transitions_relabel = config.configure_her(params_policy_buffer)
for _ in range(v['num_demos']):
# rollout is generated by expert policy
episode = expert_worker.generate_rollouts(
slice_goal=(3, 6) if v['full_space_as_goal'] else None)
# and is stored into the current expert buffer
expert_buffer.store_episode(episode)
# TODO: what is subsampling_rate
uninitialized_vars = []
for var in tf.global_variables():
try:
tf.get_default_session().run(var)
except tf.errors.FailedPreconditionError:
uninitialized_vars.append(var)
init_new_vars_op = tf.initialize_variables(uninitialized_vars)
tf.get_default_session().run(init_new_vars_op)
max_success, min_distance = evaluate_pnp(env, policy)
outer_iter = 0
logger.record_tabular("Outer_iter", outer_iter)
logger.record_tabular("Outer_Success", max_success)
logger.record_tabular("MinDisToGoal", min_distance)
logger.dump_tabular()
for outer_iter in range(1, v['outer_iters']):
logger.log("Outer itr # %i" % outer_iter)
with ExperimentLogger(inner_log_dir,
outer_iter,
snapshot_mode='last',
hold_outter_log=True):
train(
policy,
discriminator,
rollout_worker,
v['inner_iters'],
v['n_cycles'],
v['n_batches'],
v['n_batches_dis'],
policy.buffer,
expert_buffer,
empty_buffer=empty_buffer if v['on_policy_dis'] else None,
num_rollouts=v['num_rollouts'],
feasible_states=feasible_states if v['query_expert'] else None,
expert_policy=expert_policy if v['query_expert'] else None,
agent_policy=policy if v['query_agent'] else None,
train_dis_per_rollout=v['train_dis_per_rollout'],
noise_expert=v['noise_dis_agent'],
noise_agent=v['noise_dis_expert'],
sample_transitions_relabel=sample_transitions_relabel
if v['relabel_for_policy'] else None,
outer_iter=outer_iter,
annealing_coeff=v['annealing_coeff'],
q_annealing=v['q_annealing'])
print("evaluating policy performance")
logger.log("Generating heat map")
success, min_distance = evaluate_pnp(env, policy)
logger.record_tabular("Outer_iter", outer_iter)
logger.record_tabular("Outer_Success", max_success)
logger.record_tabular("MinDisToGoal", min_distance)
logger.dump_tabular()
if success > max_success:
print("% f >= %f, saving policy to params_best" %
(success, max_success))
with open(osp.join(log_dir, 'params_best.pkl'), 'wb') as f:
cloudpickle.dump({'env': env, 'policy': policy}, f)
max_success = success
report.save()
report.new_row()
def run_task(v):
random.seed(v['seed'])
np.random.seed(v['seed'])
sampling_res = 2 if 'sampling_res' not in v.keys() else v['sampling_res']
# Log performance of randomly initialized policy with FIXED goal [0.1, 0.1]
logger.log("Initializing report and plot_policy_reward...")
log_dir = logger.get_snapshot_dir() # problem with logger module here!!
report = HTMLReport(osp.join(log_dir, 'report.html'), images_per_row=4)
report.add_header("{}".format(EXPERIMENT_TYPE))
report.add_text(format_dict(v))
if v['control_mode'] == 'linear':
from sandbox.envs.maze.point_maze_env import PointMazeEnv
inner_env = normalize(PointMazeEnv(maze_id=v['maze_id'], maze_size_scaling=v['maze_scaling'], control_mode=v['control_mode']))
inner_env_test = normalize(PointMazeEnv(maze_id=v['maze_id'], maze_size_scaling=v['maze_scaling'], control_mode=v['control_mode']))
elif v['control_mode'] == 'pos':
from sandbox.envs.maze.point_maze_pos_env import PointMazeEnv
inner_env = normalize(PointMazeEnv(maze_id=v['maze_id'], maze_size_scaling=v['maze_scaling'], control_mode=v['control_mode']))
inner_env_test = normalize(PointMazeEnv(maze_id=v['maze_id'], maze_size_scaling=v['maze_scaling'], control_mode=v['control_mode']))
uniform_goal_generator = UniformStateGenerator(state_size=v['goal_size'], bounds=v['goal_range'],
center=v['goal_center'])
num_cpu = 1
if num_cpu > 1:
try:
whoami = mpi_fork(num_cpu, ['--bind-to', 'core'])
print("fancy call succeeded")
except CalledProcessError:
print("fancy version of mpi call failed, try simple version")
whoami = mpi_fork(num_cpu)
if whoami == 'parent':
sys.exit(0)
import baselines.common.tf_util as U
U.single_threaded_session().__enter__()
# Configure logging
rank = MPI.COMM_WORLD.Get_rank()
logdir = ''
if rank == 0:
if logdir or logger_b.get_dir() is None:
logger_b.configure(dir=logdir)
else:
logger_b.configure()
logdir = logger_b.get_dir()
assert logdir is not None
os.makedirs(logdir, exist_ok=True)
# Seed everything.
rank_seed = v['seed'] + 1000000 * rank
set_global_seeds(rank_seed)
feasible_states = sample_unif_feas(inner_env, 10)
if v['unif_starts']:
starts = np.random.permutation(np.array(feasible_states))[:300]
else:
starts = np.array([[0, 0]])
uniform_start_generator = UniformListStateGenerator(
starts.tolist(), persistence=v['persistence'], with_replacement=v['with_replacement'], )
# Prepare params.
def make_env(inner_env=inner_env, terminal_eps=v['terminal_eps'], terminate_env=v['terminate_env']):
return GoalStartExplorationEnv(
env=inner_env, goal_generator=uniform_goal_generator,
obs2goal_transform=lambda x: x[:v['goal_size']],
start_generator=uniform_start_generator,
obs2start_transform=lambda x: x[:v['goal_size']],
terminal_eps=terminal_eps,
distance_metric=v['distance_metric'],
extend_dist_rew=v['extend_dist_rew'],
only_feasible=v['only_feasible'],
terminate_env=terminate_env,
goal_weight=v['goal_weight'],
inner_weight=0,
append_goal_to_observation=False
)
env = make_env()
test_env = make_env(inner_env=inner_env_test, terminal_eps=1., terminate_env=True)
params = config.DEFAULT_PARAMS
params['action_l2'] = v['action_l2']
params['max_u'] = v['max_u']
params['gamma'] = v['discount']
params['env_name'] = 'FetchReach-v0'
params['replay_strategy'] = v['replay_strategy']
params['lr'] = v['lr']
params['layers'] = v['layers']
params['hidden'] = v['hidden']
params['n_cycles'] = v['n_cycles'] # cycles per epoch
params['n_batches'] = v['n_batches'] # training batches per cycle
params['batch_size'] = v['batch_size'] # per mpi thread, measured in transitions and reduced to even multiple of chunk_length.
params['n_test_rollouts'] = v['n_test_rollouts'] # changed from 10 to 3 # number of test rollouts per epoch, each consists of rollout_batch_size rollouts
# exploration
params['random_eps'] = 0.3 # percentagcone of time a random action is taken
params['noise_eps'] = v['action_noise']
params['goal_weight'] = v['goal_weight']
with open(os.path.join(logger_b.get_dir(), 'params.json'), 'w') as f:
json.dump(params, f)
params['T'] = v['horizon']
params['to_goal'] = v['to_goal']
params['nearby_action_penalty'] = v['nearby_action_penalty']
params['nearby_penalty_weight'] = v['nearby_penalty_weight']
params['nearby_p'] = v['nearby_p']
params['perturb_scale'] = v['perturb_scale']
params['cells_apart'] = v['cells_apart']
params['perturb_to_feasible'] = v['perturb_to_feasible']
params['sample_expert'] = v['sample_expert']
params['expert_batch_size'] = v['expert_batch_size']
params['bc_loss'] = v['bc_loss']
params['anneal_bc'] = v['anneal_bc']
params['gail_weight'] =v['gail_weight']
params['terminate_bootstrapping'] = v['terminate_bootstrapping']
params['mask_q'] = int(v['mode'] == 'pure_bc')
params['two_qs'] = v['two_qs']
params['anneal_discriminator'] = v['anneal_discriminator']
params['two_rs'] = v['two_qs'] or v['anneal_discriminator']
params['with_termination'] = v['rollout_terminate']
if 'clip_dis' in v and v['clip_dis']:
params['dis_bound'] = v['clip_dis']
params = config.prepare_params(params)
params['make_env'] = make_env
config.log_params(params, logger=logger_b)
dims = config.configure_dims(params)
# prepare GAIL
if v['use_s_p']:
discriminator = GAIL(dims['o'] + dims['o'] + dims['g'] if not v['only_s'] else dims['o'] + dims['g'],
dims['o'], dims['o'], dims['g'], 0., gail_loss = v['gail_reward'], use_s_p = True, only_s=v['only_s'])
else:
discriminator = GAIL(dims['o'] + dims['u'] + dims['g'] if not v['only_s'] else dims['o'] + dims['g'],
dims['o'], dims['u'], dims['g'], 0., gail_loss = v['gail_reward'], only_s=v['only_s'])
params['discriminator'] = discriminator
# configure replay buffer for expert buffer
params_expert = {k:params[k] for k in ['make_env', 'replay_k', 'discriminator', 'gail_weight', 'two_rs', 'with_termination']}
params_expert['replay_strategy'] = 'future' if v['relabel_expert'] else 'none'
params_expert['sample_g_first'] = v['relabel_expert'] and v['sample_g_first']
params_expert['zero_action_p'] = v['zero_action_p']
params_policy_buffer = {k: params[k] for k in ['make_env', 'replay_k', 'discriminator', 'gail_weight', 'two_rs', 'with_termination']}
params_policy_buffer['replay_strategy'] = 'future'
params_policy_buffer['sample_g_first'] = False
policy = config.configure_ddpg(dims=dims, params=params, clip_return=v['clip_return'], reuse=tf.AUTO_REUSE, env=env)
rollout_params = {
'exploit': False,
'use_target_net': False,
'use_demo_states': True,
'compute_Q': True,
'T': params['T'],
'weight': v['goal_weight'],
'rollout_terminate': v['rollout_terminate']
}
expert_rollout_params = {
'exploit': not v['noisy_expert'],
'use_target_net': False,
'use_demo_states': True,
'compute_Q': False,
'T': params['T'],
'weight': v['goal_weight'],
'rollout_terminate': v['rollout_terminate']
}
for name in ['T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps']:
rollout_params[name] = params[name]
expert_rollout_params[name] = params[name]
expert_rollout_params['noise_eps'] = v['expert_noise']
expert_rollout_params['random_eps'] = v['expert_eps']
rollout_worker = RolloutWorker([env], policy, dims, logger_b, **rollout_params)
# prepare expert policy, rollout worker
import joblib
if v['expert_policy'] == 'planner':
from sandbox.experiments.goals.maze.expert.maze_expert import MazeExpert
expert_policy = MazeExpert(inner_env, step_size=0.2)
else:
expert_policy = joblib.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), v['expert_policy']))['policy']
expert_rollout_worker = RolloutWorker([env], expert_policy, dims, logger_b, **expert_rollout_params)
input_shapes = dims_to_shapes(dims)
expert_sample_transitions = config.configure_her(params_expert)
buffer_shapes = {key: (v['horizon'] if key != 'o' else v['horizon'] + 1, *input_shapes[key])
for key, val in input_shapes.items()}
buffer_shapes['g'] = (buffer_shapes['g'][0], 2)
buffer_shapes['ag'] = (v['horizon'] + 1, 2)
buffer_shapes['successes'] = (v['horizon'],)
expert_buffer = ReplayBuffer(buffer_shapes, int(1e6), v['horizon'], expert_sample_transitions)
policy.expert_buffer = expert_buffer
sample_transitions_relabel = config.configure_her(params_policy_buffer)
normal_sample_transitions = policy.sample_transitions
empty_buffer = ReplayBuffer(buffer_shapes, int(1e6), v['horizon'], normal_sample_transitions)
if not v['query_expert'] or not 'gail' in v['mode']:
for i in range(v['num_demos']):
# rollout is generated by expert policy
episode = expert_rollout_worker.generate_rollouts(reset=not v['no_resets'])
# and is stored into the expert buffer
expert_buffer.store_episode(episode)
if i <= 20:
path_length = np.argmax(episode['info_goal_reached'][0])
path_length = v['horizon'] - 1 if path_length == 0 else path_length
plot_path(episode['o'][0][:path_length], report=report, obs=True, goal=episode['g'][0][0], limit=v['goal_range'], center=v['goal_center'])
report.new_row()
# TODO: what is subsampling_rate
uninitialized_vars = []
for var in tf.global_variables():
try:
tf.get_default_session().run(var)
except tf.errors.FailedPreconditionError:
uninitialized_vars.append(var)
init_new_vars_op = tf.initialize_variables(uninitialized_vars)
tf.get_default_session().run(init_new_vars_op)
outer_iter = 0
logger.log('Generating the Initial Heatmap...')
def evaluate_performance(env):
four_rooms = np.array([[-2, -2], [-13, -13]])
if v['unif_starts']:
mean_rewards, successes = [], []
for pos in four_rooms:
env.update_start_generator(FixedStateGenerator(np.array(pos)))
mr, scs = test_and_plot_policy(policy, env, horizon=v['horizon'], max_reward=v['max_reward'], sampling_res=sampling_res,
n_traj=v['n_traj'],
itr=outer_iter, report=report, limit=v['goal_range'],
center=v['goal_center'], using_gym=True,
noise=v['action_noise'], n_processes=8, log=False)
mean_rewards.append(mr)
successes.append(scs)
with logger.tabular_prefix('Outer_'):
logger.record_tabular('iter', outer_iter)
logger.record_tabular('MeanRewards', np.mean(mean_rewards))
logger.record_tabular('Success', np.mean(successes))
else:
env.update_start_generator(FixedStateGenerator(np.array([0, 0])))
_, scs = test_and_plot_policy(policy, env, horizon=v['horizon'], max_reward=v['max_reward'], sampling_res=sampling_res,
n_traj=v['n_traj'],
itr=outer_iter, report=report, limit=v['goal_range'], center=v['goal_center'],
using_gym=True,
noise=v['action_noise'], n_processes=8)
report.new_row()
env.update_start_generator(uniform_start_generator)
return scs
logger.dump_tabular(with_prefix=False)
import cloudpickle
max_success = 0.
if not v['query_expert'] and v['num_demos'] > 0:
if not v['relabel_expert']:
goals = goals_filtered = expert_buffer.buffers['g'][:v['num_demos'], 0, :]
else: # collect all states visited by the expert
goals = None
for i in range(v['num_demos']):
terminate_index = np.argmax(expert_buffer.buffers['successes'][i])
if np.logical_not(np.any(expert_buffer.buffers['successes'][i])):
terminate_index = v['horizon']
cur_goals = expert_buffer.buffers['o'][i, :terminate_index, :2]
if goals is None:
goals = cur_goals
else:
goals = np.concatenate([goals, cur_goals])
goal_state_collection = StateCollection(distance_threshold=v['coll_eps'])
goal_state_collection.append(goals)
goals_filtered = goal_state_collection.states
else:
goals_filtered = goals = np.random.permutation(np.array(feasible_states))[:300]
if v['agent_state_as_goal']:
goals = goals
else:
feasible_states = sample_unif_feas(inner_env, 10)
goals = np.random.permutation(np.array(feasible_states))[:300]
evaluate_performance(test_env)
logger.dump_tabular(with_prefix=False)
for outer_iter in range(1, v['outer_iters']):
logger.log("Outer itr # %i" % outer_iter)
with ExperimentLogger(log_dir, 'last', snapshot_mode='last', hold_outter_log=True):
logger.log("Updating the environment goal generator")
if v['unif_goals']:
env.update_goal_generator(
UniformListStateGenerator(
goals.tolist(), persistence=v['persistence'], with_replacement=v['with_replacement'],
)
)
else:
env.update_goal_generator(FixedStateGenerator(v['final_goal']))
logger.log("Training the algorithm")
train(policy, discriminator, rollout_worker, v['inner_iters'], v['n_cycles'], v['n_batches'], v['n_batches_dis'], policy.buffer, expert_buffer,
empty_buffer=empty_buffer if v['on_policy_dis'] else None, num_rollouts=v['num_rollouts'], reset=not v['no_resets'],
feasible_states=feasible_states if v['query_expert'] else None, expert_policy=expert_policy if v['query_expert'] else None,
agent_policy=policy if v['query_agent'] else None, train_dis_per_rollout=v['train_dis_per_rollout'],
noise_expert=v['noise_dis_agent'], noise_agent=v['noise_dis_expert'], sample_transitions_relabel=sample_transitions_relabel if v['relabel_for_policy'] else None,
q_annealing=v['q_annealing'], outer_iter=outer_iter, annealing_coeff=v['annealing_coeff'])
# logger.record_tabular('NonZeroRewProp', nonzeros)
logger.log('Generating the Heatmap...')
success = evaluate_performance(test_env)
if success > max_success:
print ("% f >= %f, saving policy to params_best" % (success, max_success))
with open(osp.join(log_dir, 'params_best.pkl'), 'wb') as f:
cloudpickle.dump({'env': env, 'policy': policy}, f)
max_success = success
report.new_row()
logger.dump_tabular(with_prefix=False)
def learn(*,
network,
env,
total_timesteps,
seed=None,
eval_env=None,
replay_strategy='future',
policy_save_interval=5,
clip_return=True,
demo_file=None,
override_params=None,
load_path=None,
save_path=None,
params=None,
**kwargs):
override_params = override_params or {}
if MPI is not None:
rank = MPI.COMM_WORLD.Get_rank()
num_cpu = MPI.COMM_WORLD.Get_size()
# Seed everything.
rank_seed = seed + 1000000 * rank if seed is not None else None
set_global_seeds(rank_seed)
# Prepare params.
params = {
# env
'max_u': 1., # max absolute value of actions on different coordinates
# ddpg
'layers': 3, # number of layers in the critic/actor networks
'hidden': 256, # number of neurons in each hidden layers
'network_class': 'baselines.her.actor_critic:ActorCritic',
'Q_lr': 0.001, # critic learning rate
'pi_lr': 0.001, # actor learning rate
'buffer_size': int(1E6), # for experience replay
'polyak': 0.95, # polyak averaging coefficient
'action_l2':
1.0, # quadratic penalty on actions (before rescaling by max_u)
'clip_obs': 200.,
'scope': 'ddpg', # can be tweaked for testing
'relative_goals': False,
# training
'n_cycles': 50, # per epoch
'rollout_batch_size': 2, # per mpi thread
'n_batches': 40, # training batches per cycle
'batch_size':
256, # per mpi thread, measured in transitions and reduced to even multiple of chunk_length.
'n_test_rollouts':
10, # number of test rollouts per epoch, each consists of rollout_batch_size rollouts
'test_with_polyak': False, # run test episodes with the target network
# exploration
'random_eps': 0.2, # percentage of time a random action is taken
'noise_eps':
0.3, # std of gaussian noise added to not-completely-random actions as a percentage of max_u
# HER
'replay_strategy': 'future', # supported modes: future, none
'replay_k':
4, # number of additional goals used for replay, only used if off_policy_data=future
# normalization
'norm_eps': 0.01, # epsilon used for observation normalization
'norm_clip': 5, # normalized observations are cropped to this values
'bc_loss':
0, # whether or not to use the behavior cloning loss as an auxilliary loss
'q_filter':
0, # whether or not a Q value filter should be used on the Actor outputs
'num_demo': 25, # number of expert demo episodes
'demo_batch_size':
128, #number of samples to be used from the demonstrations buffer, per mpi thread 128/1024 or 32/256
'prm_loss_weight': 0.001, #Weight corresponding to the primary loss
'aux_loss_weight':
0.0078, #Weight corresponding to the auxilliary loss also called the cloning loss
'perturb': kwargs['pert_type'],
'n_actions': kwargs['n_actions'],
}
params['replay_strategy'] = replay_strategy
if env is not None:
env_name = env.spec.id
params['env_name'] = env_name
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]
) # merge env-specific parameters in
else:
params['env_name'] = 'NuFingers_Experiment'
params.update(
**override_params) # makes it possible to override any parameter
with open(os.path.join(logger.get_dir(), 'params.json'), 'w') as f:
json.dump(params, f)
if demo_file is not None:
params['bc_loss'] = 1
params['q_filter'] = 1
params['n_cycles'] = 20
params['random_eps'] = 0.1 # chip: ON
params['noise_eps'] = 0.1 # chip: ON
# params['batch_size']: 1024
params = config.prepare_params(params)
params['rollout_batch_size'] = 1
params.update(kwargs)
config.log_params(params, logger=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/baselines/issues/314 for further details.'
)
logger.warn('****************')
logger.warn()
if env is not None:
dims = config.configure_dims(params)
else:
dims = dict(o=15, u=4, g=7, info_is_success=1)
policy = config.configure_ddpg(dims=dims,
params=params,
clip_return=clip_return)
if load_path is not None:
tf_util.load_variables(load_path)
rollout_params = {
'exploit': False,
'use_target_net': False,
'use_demo_states': True,
'compute_Q': False,
'T': params['T'],
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
'T': params['T'],
}
for name in [
'T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps'
]:
rollout_params[name] = params[name]
eval_params[name] = params[name]
eval_env = eval_env or env
print("NAME={}".format(params['env_name']))
print(rollout_params)
if params['env_name'].find('NuFingers_Experiment') == -1:
rollout_worker = RolloutWorker(env,
policy,
dims,
logger,
monitor=True,
**rollout_params)
evaluator = RolloutWorker(eval_env, policy, dims, logger,
**eval_params)
else:
rollout_worker = RolloutNuFingers(policy,
dims,
logger,
monitor=True,
**rollout_params)
evaluator = RolloutNuFingers(policy, dims, logger, **eval_params)
n_cycles = params['n_cycles']
n_epochs = total_timesteps // n_cycles // rollout_worker.T // rollout_worker.rollout_batch_size
return train(save_path=save_path,
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,
demo_file=demo_file)
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/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)
def prepare_agent(_env,
eval_env,
active,
exploration='eps_greedy',
action_l2=None,
scope=None,
ss=False,
load_path=None):
# Prepare params.
_params = copy.deepcopy(config.DEFAULT_PARAMS)
_kwargs = copy.deepcopy(kwargs)
_override_params = copy.deepcopy(override_params)
env_name = _env.spec.id
_params['env_name'] = env_name
_params['replay_strategy'] = replay_strategy
_params['ss'] = ss
if action_l2 is not None:
_params['action_l2'] = action_l2
if not active:
_params["buffer_size"] = 1
if env_name in config.DEFAULT_ENV_PARAMS:
_params.update(config.DEFAULT_ENV_PARAMS[env_name]
) # 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 f:
json.dump(_params, f)
_params = config.prepare_params(_params)
_params['rollout_batch_size'] = _env.num_envs
if demo_file is not None:
_params['bc_loss'] = 1
_params.update(_kwargs)
config.log_params(_params, logger=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/baselines/issues/314 for further details.'
)
logger.warn('****************')
logger.warn()
dims, coord_dict = config.configure_dims(_params)
_params['ddpg_params']['scope'] = scope
policy, reward_fun = config.configure_ddpg(dims=dims,
params=_params,
active=active,
clip_return=clip_return)
if load_path is not None:
tf_util.load_variables(load_path)
print(f"Loaded model: {load_path}")
rollout_params = {
'exploit': False,
'use_target_net': False,
'use_demo_states': True,
'compute_Q': False,
'exploration': exploration
}
eval_params = {
'exploit': True,
'use_target_net': _params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
}
for name in [
'T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps'
]:
rollout_params[name] = _params[name]
eval_params[name] = _params[name]
eval_env = eval_env or _env
rollout_worker = RolloutWorker(_env,
policy,
dims,
logger,
active,
monitor=True,
**rollout_params)
evaluator = RolloutWorker(eval_env, policy, dims, logger, active,
**eval_params)
return policy, rollout_worker, evaluator, _params, coord_dict, reward_fun
def main(policy_file, seed, n_test_rollouts, render, exploit, compute_q, collect_data, goal_generation, note):
set_global_seeds(seed)
# Load policy.
with open(policy_file, 'rb') as f:
policy = pickle.load(f)
env_name = policy.info['env_name']
# Prepare params.
params = config.DEFAULT_PARAMS
params['note'] = note or params['note']
if note:
with open('params/'+env_name+'/'+note+'.json', 'r') as file:
override_params = json.loads(file.read())
params.update(**override_params)
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]) # merge env-specific parameters in
params['env_name'] = env_name
goal_generation = params['goal_generation']
params = config.prepare_params(params)
config.log_params(params, logger=logger)
dims = config.configure_dims(params)
eval_params = {
'exploit': exploit, # eval: True, train: False
'use_target_net': params['test_with_polyak'], # eval/train: False
'compute_Q': compute_q, # eval: True, train: False
'rollout_batch_size': 1,
'render': render,
}
for name in ['T', 'gamma', 'noise_eps', 'random_eps']:
eval_params[name] = params[name]
evaluator = RolloutWorker(params['make_env'], policy, dims, logger, **eval_params)
evaluator.seed(seed)
# Run evaluation.
evaluator.clear_history()
num_skills = params['num_skills']
if goal_generation == 'Zero':
generated_goal = np.zeros(evaluator.g.shape)
else:
generated_goal = False
for z in range(num_skills):
assert(evaluator.rollout_batch_size==1)
z_s_onehot = np.zeros([evaluator.rollout_batch_size, num_skills])
z_s_onehot[0, z] = 1
base = os.path.splitext(policy_file)[0]
for i_test_rollouts in range(n_test_rollouts):
if render == 'rgb_array' or render == 'human':
imgs, episode = evaluator.generate_rollouts(generated_goal=generated_goal, z_s_onehot=z_s_onehot)
end = '_test_{:02d}_exploit_{}_compute_q_{}_skill_{}.avi'.format(i_test_rollouts, exploit, compute_q, z)
test_filename = base + end
save_video(imgs[0], test_filename, lib='cv2')
else:
episode = evaluator.generate_rollouts(generated_goal=generated_goal, z_s_onehot=z_s_onehot)
if collect_data:
end = '_test_{:02d}_exploit_{}_compute_q_{}_skill_{}.txt'.format(i_test_rollouts, exploit, compute_q, z)
test_filename = base + end
with open(test_filename, 'w') as file:
file.write(json.dumps(episode['o'].tolist()))
# record logs
for key, val in evaluator.logs('test'):
logger.record_tabular(key, np.mean(val))
logger.dump_tabular()
def launch(env_name,
n_epochs,
num_cpu,
seed,
replay_strategy,
policy_save_interval,
clip_return,
temperature,
prioritization,
binding,
logging,
version,
dump_buffer,
n_cycles,
rank_method,
fit_interval,
override_params={},
save_policies=True):
# Fork for multi-CPU MPI implementation.
if num_cpu > 1:
whoami = mpi_fork(num_cpu, binding)
if whoami == 'parent':
sys.exit(0)
import baselines.common.tf_util as U
U.single_threaded_session().__enter__()
rank = MPI.COMM_WORLD.Get_rank()
# Configure logging
if logging:
logdir = 'logs/'+str(env_name)+'-prioritization'+str(prioritization)+'-replay_strategy'+str(replay_strategy)+\
'-n_epochs'+str(n_epochs)+'-num_cpu'+str(num_cpu)+'-seed'+str(seed)+'-version'+str(version)
else:
logdir = osp.join(
tempfile.gettempdir(),
datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M-%S-%f"))
if rank == 0:
if logdir or logger.get_dir() is None:
logger.configure(dir=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_name
params['replay_strategy'] = replay_strategy
params['temperature'] = temperature
params['prioritization'] = prioritization
params['binding'] = binding
params['max_timesteps'] = n_epochs * params['n_cycles'] * params[
'n_batches'] * num_cpu
params['version'] = version
params['dump_buffer'] = dump_buffer
params['n_cycles'] = n_cycles
params['rank_method'] = rank_method
params['fit_interval'] = fit_interval
params['n_epochs'] = n_epochs
params['num_cpu'] = num_cpu
if params['dump_buffer']:
params['alpha'] = 0
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]
) # 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 f:
json.dump(params, f)
params = config.prepare_params(params)
config.log_params(params, logger=logger)
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,
'T': params['T'],
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
'T': params['T'],
}
for name in [
'T', '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(logdir=logdir,
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,
num_cpu=num_cpu,
dump_buffer=dump_buffer,
rank_method=rank_method,
fit_interval=fit_interval,
prioritization=prioritization)
def learn(*, network, env, total_timesteps,
seed=None,
eval_env=None,
replay_strategy='future',
policy_save_interval=5,
clip_return=True,
demo_file=None,
override_params=None,
load_path=None,
save_path=None,
**kwargs
):
override_params = override_params or {}
if MPI is not None:
rank = MPI.COMM_WORLD.Get_rank()
num_cpu = MPI.COMM_WORLD.Get_size()
# Seed everything.
rank_seed = seed + 1000000 * rank if seed is not None else None
set_global_seeds(rank_seed)
# Prepare params.
params = config.DEFAULT_PARAMS
env_name = env.spec.id
params['env_name'] = env_name
params['replay_strategy'] = replay_strategy
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]) # 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 f:
json.dump(params, f)
params = config.prepare_params(params)
params['rollout_batch_size'] = env.num_envs
if demo_file is not None:
params['bc_loss'] = 1
params.update(kwargs)
config.log_params(params, logger=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/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)
if load_path is not None:
tf_util.load_variables(load_path)
rollout_params = {
'exploit': False,
'use_target_net': False,
'use_demo_states': True,
'compute_Q': False,
'T': params['T'],
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
'T': params['T'],
}
for name in ['T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps']:
rollout_params[name] = params[name]
eval_params[name] = params[name]
eval_env = eval_env or env
rollout_worker = RolloutWorker(env, policy, dims, logger, monitor=True, **rollout_params)
evaluator = RolloutWorker(eval_env, policy, dims, logger, **eval_params)
n_cycles = params['n_cycles']
n_epochs = total_timesteps // n_cycles // rollout_worker.T // rollout_worker.rollout_batch_size
return train(
save_path=save_path, 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, demo_file=demo_file)
def launch(env, trial_id, n_epochs, num_cpu, seed, policy_save_interval, clip_return, normalize_obs,
structure, task_selection, goal_selection, goal_replay, task_replay, perturb, save_policies=True):
# 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)
import baselines.common.tf_util as U
U.single_threaded_session().__enter__()
rank = MPI.COMM_WORLD.Get_rank()
# Configure logging
if rank == 0:
save_dir = find_save_path('./save/' + env + "/", trial_id)
logger.configure(dir=save_dir)
else:
save_dir = None
# Seed everything.
rank_seed = seed + 1000000 * rank
set_global_seeds(rank_seed)
# Prepare params, add main function arguments and log all parameters
if structure == 'curious' or structure == 'task_experts':
params = config.MULTI_TASK_PARAMS
else:
params = config.DEFAULT_PARAMS
time = str(datetime.datetime.now())
params['time'] = time
params['env_name'] = env
params['task_selection'] = task_selection
params['goal_selection'] = goal_selection
params['task_replay'] = task_replay
params['goal_replay'] = goal_replay
params['structure'] = structure
params['normalize_obs'] = normalize_obs
params['num_cpu'] = num_cpu
params['clip_return'] = clip_return
params['trial_id'] = trial_id
params['seed'] = seed
if rank == 0:
with open(os.path.join(logger.get_dir(), 'params.json'), 'w') as f:
json.dump(params, f)
params = config.prepare_params(params)
params['ddpg_params']['normalize_obs'] = normalize_obs
if rank == 0:
config.log_params(params, logger=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 Colas et al. (2018, https://arxiv.org/abs/1810.06284) ' +
'were obtained with --num_cpu 19. This makes a significant difference and if you ' +
'are looking to reproduce those results, be aware of this.')
logger.warn('****************')
logger.warn()
dims = config.configure_dims(params)
buffers = config.configure_buffer(dims=dims, params=params)
# creates several policies with shared buffers in the task-experts structure, otherwise use just one policy
if structure == 'task_experts':
policy = [config.configure_ddpg(dims=dims, params=params, buffers=buffers, clip_return=clip_return, t_id=i) for i in range(params['nb_tasks'])]
else:
policy = config.configure_ddpg(dims=dims, params=params, buffers=buffers, clip_return=clip_return)
rollout_params = {
'exploit': False,
'use_target_net': False,
'use_demo_states': True,
'compute_Q': False,
'T': params['T'],
'structure': structure,
'task_selection': task_selection,
'goal_selection': goal_selection,
'queue_length': params['queue_length'],
'eval': False,
'eps_task': params['eps_task']
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
'T': params['T'],
'structure' : structure,
'task_selection': task_selection,
'goal_selection' : goal_selection,
'queue_length': params['queue_length'],
'eval': True,
}
for name in ['T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps']:
rollout_params[name] = params[name]
eval_params[name] = params[name]
if structure == 'task_experts':
# create one rollout worker per policy/task
rollout_worker = [RolloutWorker(params['make_env'], policy[i], dims, logger, unique_task=i, **rollout_params) for i in range(params['nb_tasks'])]
for i in range(params['nb_tasks']):
rollout_worker[i].seed(rank_seed + i)
else:
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 + 100)
train(logdir=save_dir, 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'], perturbation_study=perturb,
policy_save_interval=policy_save_interval, save_policies=save_policies, structure=structure, task_selection=task_selection, params=params)
def launch(
env_name, logdir, n_epochs, num_cpu, seed, replay_strategy, policy_save_interval, clip_return,
override_params={}, save_policies=True
):
# Fork for multi-CPU MPI implementation.
if num_cpu > 1:
whoami = mpi_fork(num_cpu)
if whoami == 'parent':
sys.exit(0)
import baselines.common.tf_util as U
U.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(dir=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_name
params['replay_strategy'] = replay_strategy
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]) # 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 f:
json.dump(params, f)
params = config.prepare_params(params)
config.log_params(params, logger=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/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,
'T': params['T'],
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
'T': params['T'],
}
for name in ['T', '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(
logdir=logdir, 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)
def launch(env,
logdir,
n_epochs,
num_cpu,
seed,
replay_strategy,
policy_save_interval,
clip_return,
override_params={},
save_policies=True):
# 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)
import baselines.common.tf_util as U
U.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(dir=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 f:
# json.dump(params, f)
# {'Q_lr': 0.001, 'action_l2': 1.0, 'batch_size': 256, 'buffer_size': 1000000, 'clip_obs': 200.0,
# 'env_name': u'GazeboSmartBotPinc...Kinect-v0', 'hidden': 256, 'layers': 3, 'max_u': 1.0, 'n_batches': 40,
# 'n_cycles': 50, 'n_test_rollouts': 10, 'network_class': 'baselines.her.actor...torCritic', 'noise_eps': 0.2, ...}
params = config.prepare_params(
params
) # {'T': 200, '_Q_lr': 0.001, '_action_l2': 1.0, '_batch_size': 256, '_buffer_size': 1000000, '_clip_obs': 200.0, '_hidden': 256, '_layers': 3, '_max_u': 1.0, '_network_class': 'baselines.her.actor...torCritic', '_norm_clip': 5, '_norm_eps': 0.01, '_pi_lr': 0.001, '_polyak': 0.95, ...}
config.log_params(params, logger=logger)
# T: 200
# _Q_lr: 0.001
# _action_l2: 1.0
# _batch_size: 256
# _buffer_size: 1000000
# _clip_obs: 200.0
# _hidden: 256
# _layers: 3
# _max_u: 1.0
# _network_class: baselines.her.actor_critic:ActorCritic
# _norm_clip: 5
# _norm_eps: 0.01
# _pi_lr: 0.001
# _polyak: 0.95
# _relative_goals: False
# _scope: ddpg
# ddpg_params: {'layers': 3, 'norm_clip': 5, 'action_l2': 1.0, 'pi_lr': 0.001, 'norm_eps': 0.01, 'batch_size': 256, 'polyak': 0.95, 'clip_obs': 200.0, 'network_class': 'baselines.her.actor_critic:ActorCritic', 'max_u': 1.0, 'Q_lr': 0.001, 'scope': 'ddpg', 'buffer_size': 1000000, 'hidden': 256, 'relative_goals': False}
# env_name: GazeboSmartBotPincherKinect-v0
# gamma: 0.995
# make_env: <function make_env at 0x7f7de3cb2050>
# n_batches: 40
# n_cycles: 50
# n_test_rollouts: 10
# noise_eps: 0.2
# random_eps: 0.3
# replay_k: 4
# replay_strategy: future
# rollout_batch_size: 2
# test_with_polyak: False
# 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/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,
'T': params['T'],
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
'T': params['T'],
}
for name in [
'T', '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(logdir=logdir,
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)
def learn(*,
network,
env,
total_timesteps,
seed=None,
eval_env=None,
replay_strategy='future',
policy_save_interval=5,
clip_return=True,
demo_file=None,
override_params=None,
load_path=None,
save_path=None,
**kwargs):
override_params = override_params or {}
if MPI is not None:
rank = MPI.COMM_WORLD.Get_rank()
num_cpu = MPI.COMM_WORLD.Get_size()
# Seed everything.
rank_seed = seed + 1000000 * rank if seed is not None else None
set_global_seeds(rank_seed)
# Prepare params.
params = config.DEFAULT_PARAMS
env_name = env.specs[0].id
params['env_name'] = env_name
params['replay_strategy'] = replay_strategy
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]
) # 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 f:
json.dump(params, f)
params = config.prepare_params(params)
params['rollout_batch_size'] = env.num_envs
if demo_file is not None:
params['bc_loss'] = 1
params.update(kwargs)
config.log_params(params, logger=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/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)
if load_path is not None:
tf_util.load_variables(load_path)
rollout_params = {
'exploit': False,
'use_target_net': False,
'use_demo_states': True,
'compute_Q': False,
'T': params['T'],
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
'T': params['T'],
}
for name in [
'T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps'
]:
rollout_params[name] = params[name]
eval_params[name] = params[name]
eval_env = eval_env or env
rollout_worker = RolloutWorker(env,
policy,
dims,
logger,
monitor=True,
**rollout_params)
evaluator = RolloutWorker(eval_env, policy, dims, logger, **eval_params)
n_cycles = params['n_cycles']
n_epochs = total_timesteps // n_cycles // rollout_worker.T // rollout_worker.rollout_batch_size
return train(save_path=save_path,
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,
demo_file=demo_file)
def main(policy_file, seed, n_test_rollouts, render, fname):
set_global_seeds(seed)
percent = 0.001
obs_npy, achieved_goal_npy, goal_npy, action_npy, reward_npy, info_npy = [], [], [], [], [], []
# Load policy.
with open(policy_file, 'rb') as f:
policy = pickle.load(f)
env_name = policy.info['env_name']
# Prepare params.
params = config.DEFAULT_PARAMS
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]) # merge env-specific parameters in
params['env_name'] = env_name
params = config.prepare_params(params)
config.log_params(params, logger=logger)
dims = config.configure_dims(params)
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'compute_Q': True,
'rollout_batch_size': 1,
'render': bool(render),
}
for name in ['T', 'gamma', 'noise_eps', 'random_eps']:
eval_params[name] = params[name]
evaluator = RolloutWorker(params['make_env'], policy, dims, logger, **eval_params)
evaluator.seed(seed)
# Run evaluation.
evaluator.clear_history()
print("Progress Rate ::")
#print("*")
for i in tqdm(range(n_test_rollouts)):
episode = evaluator.generate_rollouts()
obs, goals, achieved_goals, acts, reward_arr, successes = clean_data(episode)
obs_npy.append(np.array(obs))
goal_npy.append(np.array(goals))
achieved_goal_npy.append(np.array(achieved_goals))
action_npy.append(np.array(acts))
reward_npy.append(np.array(reward_arr))
info_npy.append(np.array(successes))
#print(int(n_test_rollouts * percent))
#print(i)
if int(i) == int(n_test_rollouts * percent):
#print("*")
percent += 0.001
# Saving in a file
np.savez(fname, obs= obs_npy, g = goal_npy, ag=achieved_goal_npy, acts = action_npy, rew =reward_npy, info = info_npy)
# record logs
for key, val in evaluator.logs('test'):
logger.record_tabular(key, np.mean(val))
logger.dump_tabular()