def experiment(specs):
with open(path.join(specs['specific_exp_dir'], 'variant.json'), 'r') as f:
variant = json.load(f)
variant['algo_params']['do_not_train'] = True
variant['seed'] = specs['seed']
policy = joblib.load(path.join(specs['specific_exp_dir'],
'params.pkl'))['exploration_policy']
assert False, 'Do you really wanna make it deterministic?'
policy = MakeDeterministic(policy)
env_specs = variant['env_specs']
env, _ = get_env(env_specs)
training_env, _ = get_env(env_specs)
variant['algo_params']['replay_buffer_size'] = int(
np.floor(specs['num_episodes'] *
variant['algo_params']['max_path_length'] /
specs['subsampling']))
# Hack until I figure out how things are gonna be in general then I'll clean it up
if 'policy_uses_pixels' not in variant['algo_params']:
variant['algo_params']['policy_uses_pixels'] = False
if 'policy_uses_task_params' not in variant['algo_params']:
variant['algo_params']['policy_uses_task_params'] = False
if 'concat_task_params_to_policy_obs' not in variant['algo_params']:
variant['algo_params']['concat_task_params_to_policy_obs'] = False
replay_buffer = ExpertReplayBuffer(
variant['algo_params']['replay_buffer_size'],
env,
subsampling=specs['subsampling'],
policy_uses_pixels=variant['algo_params']['policy_uses_pixels'],
policy_uses_task_params=variant['algo_params']
['policy_uses_task_params'],
concat_task_params_to_policy_obs=variant['algo_params']
['concat_task_params_to_policy_obs'],
)
variant['algo_params']['freq_saving'] = 1
algorithm = ExpertTrajGeneratorAlgorithm(
env=env,
training_env=training_env,
exploration_policy=policy,
replay_buffer=replay_buffer,
max_num_episodes=specs['num_episodes'],
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train()
return 1
def experiment(variant):
with open('expert_demos_listing.yaml', 'r') as f:
listings = yaml.load(f.read())
demos_path = listings[variant['expert_name']]['file_paths'][
variant['expert_idx']]
print(demos_path)
buffer_save_dict = joblib.load(demos_path)
target_state_buffer = buffer_save_dict['data']
# target_state_buffer /= variant['rescale']
state_indices = torch.LongTensor(variant['state_indices'])
env_specs = variant['env_specs']
env = get_env(env_specs)
env.seed(env_specs['eval_env_seed'])
print('\n\nEnv: {}'.format(env_specs['env_name']))
print('kwargs: {}'.format(env_specs['env_kwargs']))
print('Obs Space: {}'.format(env.observation_space))
print('Act Space: {}\n\n'.format(env.action_space))
policy = joblib.load(variant['policy_checkpoint'])['exploration_policy']
if variant['eval_deterministic']:
policy = MakeDeterministic(policy)
policy.to(ptu.device)
eval_sampler = PathSampler(env,
policy,
variant['num_eval_steps'],
variant['max_path_length'],
no_terminal=variant['no_terminal'],
render=variant['render'],
render_kwargs=variant['render_kwargs'])
test_paths = eval_sampler.obtain_samples()
obs = []
for path in test_paths:
obs += path['observations']
x = [o[0] for o in obs]
y = [o[1] for o in obs]
fig, ax = plt.subplots(figsize=(6, 6))
plt.scatter(x, y)
plt.xlim(-1.25, 20)
plt.ylim(-1.25, 10)
ax.set_yticks([0, 5, 10])
ax.set_xticks([0, 5, 10, 15, 20])
plt.savefig('./figs/' + variant['env_specs']['task_name'] + '.pdf',
bbox_inches='tight')
return 1
def experiment(variant):
env_specs = variant['env_specs']
env = get_env(env_specs)
env.seed(env_specs['eval_env_seed'])
print('\n\nEnv: {}'.format(env_specs['env_name']))
print('kwargs: {}'.format(env_specs['env_kwargs']))
print('Obs Space: {}'.format(env.observation_space))
print('Act Space: {}\n\n'.format(env.action_space))
if variant['scale_env_with_demo_stats']:
with open('expert_demos_listing.yaml', 'r') as f:
listings = yaml.load(f.read())
expert_demos_path = listings[variant['expert_name']]['file_paths'][
variant['expert_idx']]
buffer_save_dict = joblib.load(expert_demos_path)
env = ScaledEnv(
env,
obs_mean=buffer_save_dict['obs_mean'],
obs_std=buffer_save_dict['obs_std'],
acts_mean=buffer_save_dict['acts_mean'],
acts_std=buffer_save_dict['acts_std'],
)
policy = joblib.load(variant['policy_checkpoint'])['exploration_policy']
if variant['eval_deterministic']:
policy = MakeDeterministic(policy)
policy.to(ptu.device)
eval_sampler = PathSampler(env,
policy,
variant['num_eval_steps'],
variant['max_path_length'],
no_terminal=variant['no_terminal'],
render=variant['render'],
render_kwargs=variant['render_kwargs'])
test_paths = eval_sampler.obtain_samples()
average_returns = eval_util.get_average_returns(test_paths)
print(average_returns)
return 1
def experiment(variant):
with open(EXPERT_LISTING_YAML_PATH, 'r') as f:
listings = yaml.load(f.read())
expert_dir = listings[variant['expert_name']]['exp_dir']
specific_run = listings[variant['expert_name']]['seed_runs'][
variant['expert_seed_run_idx']]
file_to_load = path.join(expert_dir, specific_run, 'extra_data.pkl')
extra_data = joblib.load(file_to_load)
# this script is for the non-meta-learning GAIL
expert_buffer = extra_data['train']
# set up the env
env_specs = variant['env_specs']
if env_specs['train_test_env']:
env, training_env = get_env(env_specs)
else:
env, _ = get_env(env_specs)
training_env, _ = get_env(env_specs)
env.seed(variant['seed'])
training_env.seed(variant['seed'])
print(env.observation_space)
if variant['scale_env_with_given_demo_stats']:
assert not env_specs['normalized']
env = ScaledEnv(
env,
obs_mean=extra_data['obs_mean'],
obs_std=extra_data['obs_std'],
acts_mean=extra_data['acts_mean'],
acts_std=extra_data['acts_std'],
)
training_env = ScaledEnv(
training_env,
obs_mean=extra_data['obs_mean'],
obs_std=extra_data['obs_std'],
acts_mean=extra_data['acts_mean'],
acts_std=extra_data['acts_std'],
)
# compute obs_dim and action_dim
if isinstance(env.observation_space, Dict):
if not variant['algo_params']['policy_uses_pixels']:
obs_dim = int(np.prod(env.observation_space.spaces['obs'].shape))
if variant['algo_params']['policy_uses_task_params']:
if variant['algo_params']['concat_task_params_to_policy_obs']:
obs_dim += int(
np.prod(env.observation_space.
spaces['obs_task_params'].shape))
else:
raise NotImplementedError()
else:
raise NotImplementedError()
else:
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
print(obs_dim, action_dim)
sleep(3)
# set up the policy models
policy_net_size = variant['policy_net_size']
hidden_sizes = [policy_net_size] * variant['policy_num_hidden_layers']
policy = ReparamTanhMultivariateGaussianPolicy(
hidden_sizes=hidden_sizes,
obs_dim=obs_dim,
action_dim=action_dim,
)
# set up the discriminator models
disc_model = StandardAIRLDisc(
obs_dim + action_dim,
num_layer_blocks=variant['disc_num_blocks'],
hid_dim=variant['disc_hid_dim'],
hid_act=variant['disc_hid_act'],
use_bn=variant['disc_use_bn'],
clamp_magnitude=variant['disc_clamp_magnitude'])
print(disc_model)
print(disc_model.clamp_magnitude)
# set up the AdvBC algorithm
algorithm = AdvBC(env,
policy,
disc_model,
expert_buffer,
training_env=training_env,
wrap_absorbing=variant['wrap_absorbing_state'],
**variant['algo_params'])
print(algorithm.use_target_disc)
print(algorithm.soft_target_disc_tau)
print(algorithm.exploration_policy)
print(algorithm.eval_policy)
print(algorithm.disc_optimizer.defaults['lr'])
print(algorithm.policy_optimizer.defaults['lr'])
# train
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train()
return 1
def experiment(variant):
with open('expert_demos_listing.yaml', 'r') as f:
listings = yaml.load(f.read())
demos_path = listings[variant['expert_name']]['file_paths'][
variant['expert_idx']]
print(demos_path)
buffer_save_dict = joblib.load(demos_path)
target_state_buffer = buffer_save_dict['data']
# target_state_buffer /= variant['rescale']
state_indices = torch.LongTensor(variant['state_indices'])
env_specs = variant['env_specs']
env = get_env(env_specs)
env.seed(env_specs['eval_env_seed'])
training_env = get_env(env_specs)
training_env.seed(env_specs['training_env_seed'])
print('\n\nEnv: {}'.format(env_specs['env_name']))
print('kwargs: {}'.format(env_specs['env_kwargs']))
print('Obs Space: {}'.format(env.observation_space))
print('Act Space: {}\n\n'.format(env.action_space))
obs_space = env.observation_space
act_space = env.action_space
assert not isinstance(obs_space, Dict)
assert len(obs_space.shape) == 1
assert len(act_space.shape) == 1
obs_dim = obs_space.shape[0]
action_dim = act_space.shape[0]
# build the policy models
net_size = variant['policy_net_size']
num_hidden = variant['policy_num_hidden_layers']
qf1 = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
qf2 = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
vf = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim,
output_size=1,
)
policy = ReparamTanhMultivariateGaussianPolicy(
hidden_sizes=num_hidden * [net_size],
obs_dim=obs_dim,
action_dim=action_dim,
)
# build the energy model
if variant['ebil_params']['mode'] == 'deen':
"""
ebm_model = MLPEBM(
obs_dim + action_dim if not variant['ebil_params']['state_only'] else 2*obs_dim,
num_layer_blocks=variant['ebm_num_blocks'],
hid_dim=variant['ebm_hid_dim'],
hid_act=variant['ebm_hid_act'],
use_bn=variant['ebm_use_bn'],
clamp_magnitude=variant['ebm_clamp_magnitude'],
)
"""
ebm_exp_name = 'ebm-deen-' + variant['env_specs'][
'env_name'] + '-' + str(
variant['expert_traj_num']) + '-train--sigma-' + str(
variant['ebm_sigma'])
ebm_dir = os.path.join(config.LOCAL_LOG_DIR, ebm_exp_name)
load_ebm_dir = ebm_dir
load_epoch = variant['ebm_epoch']
load_name = 'itr_{}.pkl'.format(load_epoch)
if load_epoch == 'best':
load_name = 'best.pkl'
load_ebm_path = os.path.join(load_ebm_dir, load_name)
load_ebm_pkl = joblib.load(load_ebm_path, mmap_mode='r')
ebm_model = load_ebm_pkl['ebm']
else:
raise NotImplementedError
# Test
if variant['test']:
batch_data = target_state_buffer / variant['rescale']
obs = torch.Tensor(batch_data[:1000]).to(ptu.device)
print("Not expert data", ebm_model(obs * 200).mean().item())
print("Expert data", ebm_model(obs).mean().item())
exit(1)
# set up the algorithm
trainer = SoftActorCritic(policy=policy,
qf1=qf1,
qf2=qf2,
vf=vf,
**variant['sac_params'])
algorithm = EBIL(env=env,
training_env=training_env,
exploration_policy=policy,
rew_func=variant['rew_func'],
cons=variant['cons'],
rescale=variant['rescale'],
ebm=ebm_model,
policy_trainer=trainer,
target_state_buffer=target_state_buffer,
state_indices=state_indices,
**variant['ebil_params'])
if ptu.gpu_enabled():
algorithm.to(ptu.device)
algorithm.train()
return 1
def experiment(variant):
with open('expert_demos_listing.yaml', 'r') as f:
listings = yaml.load(f.read())
expert_demos_path = listings[variant['expert_name']]['file_paths'][
variant['expert_idx']]
buffer_save_dict = joblib.load(expert_demos_path)
expert_replay_buffer = buffer_save_dict['train']
if 'minmax_env_with_demo_stats' in variant.keys():
if variant['minmax_env_with_demo_stats']:
assert 'norm_train' in buffer_save_dict.keys()
expert_replay_buffer = buffer_save_dict['norm_train']
env_specs = variant['env_specs']
env = get_env(env_specs)
env.seed(env_specs['eval_env_seed'])
training_env = get_env(env_specs)
training_env.seed(env_specs['training_env_seed'])
print('\n\nEnv: {}'.format(env_specs['env_name']))
print('kwargs: {}'.format(env_specs['env_kwargs']))
print('Obs Space: {}'.format(env.observation_space))
print('Act Space: {}\n\n'.format(env.action_space))
if variant['scale_env_with_demo_stats']:
env = ScaledEnv(
env,
obs_mean=buffer_save_dict['obs_mean'],
obs_std=buffer_save_dict['obs_std'],
acts_mean=buffer_save_dict['acts_mean'],
acts_std=buffer_save_dict['acts_std'],
)
training_env = ScaledEnv(
training_env,
obs_mean=buffer_save_dict['obs_mean'],
obs_std=buffer_save_dict['obs_std'],
acts_mean=buffer_save_dict['acts_mean'],
acts_std=buffer_save_dict['acts_std'],
)
elif variant['minmax_env_with_demo_stats']:
env = MinmaxEnv(
env,
obs_min=buffer_save_dict['obs_min'],
obs_max=buffer_save_dict['obs_max'],
)
training_env = MinmaxEnv(
training_env,
obs_min=buffer_save_dict['obs_min'],
obs_max=buffer_save_dict['obs_max'],
)
obs_space = env.observation_space
act_space = env.action_space
assert not isinstance(obs_space, Dict)
assert len(obs_space.shape) == 1
assert len(act_space.shape) == 1
obs_dim = obs_space.shape[0]
action_dim = act_space.shape[0]
# build the policy models
net_size = variant['policy_net_size']
num_hidden = variant['policy_num_hidden_layers']
qf1 = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
qf2 = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
vf = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim,
output_size=1,
)
policy = ReparamTanhMultivariateGaussianPolicy(
hidden_sizes=num_hidden * [net_size],
obs_dim=obs_dim,
action_dim=action_dim,
)
# build the critic model
critic_model = MLPDisc(variant['policy_net_size'],
num_layer_blocks=variant['critic_num_blocks'],
hid_dim=variant['critic_hid_dim'],
hid_act=variant['critic_hid_act'],
use_bn=variant['critic_use_bn'])
algorithm = BC(env=env,
training_env=training_env,
exploration_policy=policy,
critic=critic_model,
expert_replay_buffer=expert_replay_buffer,
**variant['adp_bc_params'])
if ptu.gpu_enabled():
algorithm.to(ptu.device)
algorithm.train()
return 1
def experiment(variant):
with open('expert_demos_listing.yaml', 'r') as f:
listings = yaml.load(f.read())
expert_demos_path = listings[variant['expert_name']]['file_paths'][
variant['expert_idx']]
buffer_save_dict = joblib.load(expert_demos_path)
expert_replay_buffer = buffer_save_dict['train']
env_specs = variant['env_specs']
env = get_env(env_specs)
env.seed(env_specs['eval_env_seed'])
training_env = get_env(env_specs)
training_env.seed(env_specs['training_env_seed'])
print('\n\nEnv: {}'.format(env_specs['env_name']))
print('kwargs: {}'.format(env_specs['env_kwargs']))
print('Obs Space: {}'.format(env.observation_space))
print('Act Space: {}\n\n'.format(env.action_space))
if variant['scale_env_with_demo_stats']:
env = ScaledEnv(
env,
obs_mean=buffer_save_dict['obs_mean'],
obs_std=buffer_save_dict['obs_std'],
acts_mean=buffer_save_dict['acts_mean'],
acts_std=buffer_save_dict['acts_std'],
)
training_env = ScaledEnv(
training_env,
obs_mean=buffer_save_dict['obs_mean'],
obs_std=buffer_save_dict['obs_std'],
acts_mean=buffer_save_dict['acts_mean'],
acts_std=buffer_save_dict['acts_std'],
)
obs_space = env.observation_space
act_space = env.action_space
assert not isinstance(obs_space, Dict)
assert len(obs_space.shape) == 1
assert len(act_space.shape) == 1
obs_dim = obs_space.shape[0]
action_dim = act_space.shape[0]
# build the policy models
net_size = variant['policy_net_size']
num_hidden = variant['policy_num_hidden_layers']
policy = ReparamTanhMultivariateGaussianPolicy(
hidden_sizes=num_hidden * [net_size],
obs_dim=obs_dim,
action_dim=action_dim,
)
algorithm = BC(env=env,
training_env=training_env,
exploration_policy=policy,
expert_replay_buffer=expert_replay_buffer,
**variant['bc_params'])
if ptu.gpu_enabled():
algorithm.to(ptu.device)
algorithm.train()
return 1
def experiment(variant):
with open('expert_demos_listing.yaml', 'r') as f:
listings = yaml.load(f.read())
expert_demos_path = listings[variant['expert_name']]['file_paths'][variant['expert_idx']]
buffer_save_dict = joblib.load(expert_demos_path)
expert_replay_buffer = buffer_save_dict['train']
if 'minmax_env_with_demo_stats' in variant.keys():
if variant['minmax_env_with_demo_stats']:
print('Use minmax envs')
assert 'norm_train' in buffer_save_dict.keys()
expert_replay_buffer = buffer_save_dict['norm_train']
env_specs = variant['env_specs']
env = get_env(env_specs)
env.seed(env_specs['eval_env_seed'])
training_env = get_env(env_specs)
training_env.seed(env_specs['training_env_seed'])
print('\n\nEnv: {}'.format(env_specs['env_name']))
print('kwargs: {}'.format(env_specs['env_kwargs']))
print('Obs Space: {}'.format(env.observation_space))
print('Act Space: {}\n\n'.format(env.action_space))
if variant['scale_env_with_demo_stats']:
env = ScaledEnv(
env,
obs_mean=buffer_save_dict['obs_mean'],
obs_std=buffer_save_dict['obs_std'],
acts_mean=buffer_save_dict['acts_mean'],
acts_std=buffer_save_dict['acts_std'],
)
training_env = ScaledEnv(
training_env,
obs_mean=buffer_save_dict['obs_mean'],
obs_std=buffer_save_dict['obs_std'],
acts_mean=buffer_save_dict['acts_mean'],
acts_std=buffer_save_dict['acts_std'],
)
elif variant['minmax_env_with_demo_stats']:
env = MinmaxEnv(
env,
obs_min=buffer_save_dict['obs_min'],
obs_max=buffer_save_dict['obs_max'],
)
training_env = MinmaxEnv(
training_env,
obs_min=buffer_save_dict['obs_min'],
obs_max=buffer_save_dict['obs_max'],
)
obs_space = env.observation_space
act_space = env.action_space
assert not isinstance(obs_space, Dict)
assert len(obs_space.shape) == 1
assert len(act_space.shape) == 1
obs_dim = obs_space.shape[0]
action_dim = act_space.shape[0]
# build the policy models
net_size = variant['policy_net_size']
num_hidden = variant['policy_num_hidden_layers']
policy = ReparamTanhMultivariateGaussianPolicy(
hidden_sizes=num_hidden * [net_size],
obs_dim=obs_dim,
action_dim=action_dim,
)
input_dim = obs_dim + action_dim if not variant['ebm_params']['state_only'] else 2*obs_dim
# build the energy model
if (variant['ebm_params']['mode']) == 'deen':
ebm_model = MLPEBM(
input_dim,
num_layer_blocks=variant['ebm_num_blocks'],
hid_dim=variant['ebm_hid_dim'],
hid_act=variant['ebm_hid_act'],
use_bn=variant['ebm_use_bn'],
clamp_magnitude=variant['ebm_clamp_magnitude']
)
algorithm = EBMLearn(
env=env,
training_env=training_env,
ebm=ebm_model,
input_dim = input_dim,
exploration_policy=policy,
sigma=variant['sigma'],
expert_replay_buffer=expert_replay_buffer,
**variant['ebm_params']
)
# build the energy model
elif (variant['ebm_params']['mode']) == 'ae':
ebm_model = MLPAE(
input_dim,
num_layer_blocks=variant['ebm_num_blocks'],
hid_dim=variant['ebm_hid_dim'],
hid_act=variant['ebm_hid_act'],
use_bn=variant['ebm_use_bn'],
)
algorithm = EBMLearn(
env=env,
training_env=training_env,
ebm=ebm_model,
input_dim = input_dim,
exploration_policy=policy,
sigma=None,
expert_replay_buffer=expert_replay_buffer,
**variant['ebm_params']
)
if ptu.gpu_enabled():
algorithm.to(ptu.device)
algorithm.train()
return 1
def experiment(variant):
expert_buffer = joblib.load(variant['xy_data_path'])['xy_data']
# set up the env
env_specs = variant['env_specs']
if env_specs['train_test_env']:
env, training_env = get_env(env_specs)
else:
env, _ = get_env(env_specs)
training_env, _ = get_env(env_specs)
env.seed(variant['seed'])
training_env.seed(variant['seed'])
print(env.observation_space)
if variant['scale_env_with_given_demo_stats']:
assert False
assert not env_specs['normalized']
env = ScaledEnv(
env,
obs_mean=extra_data['obs_mean'],
obs_std=extra_data['obs_std'],
acts_mean=extra_data['acts_mean'],
acts_std=extra_data['acts_std'],
)
training_env = ScaledEnv(
training_env,
obs_mean=extra_data['obs_mean'],
obs_std=extra_data['obs_std'],
acts_mean=extra_data['acts_mean'],
acts_std=extra_data['acts_std'],
)
# compute obs_dim and action_dim
if isinstance(env.observation_space, Dict):
if not variant['algo_params']['policy_uses_pixels']:
obs_dim = int(np.prod(env.observation_space.spaces['obs'].shape))
if variant['algo_params']['policy_uses_task_params']:
if variant['algo_params']['concat_task_params_to_policy_obs']:
obs_dim += int(
np.prod(env.observation_space.
spaces['obs_task_params'].shape))
else:
raise NotImplementedError()
else:
raise NotImplementedError()
else:
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
print(obs_dim, action_dim)
sleep(3)
# set up the policy models
policy_net_size = variant['policy_net_size']
hidden_sizes = [policy_net_size] * variant['policy_num_hidden_layers']
qf1 = FlattenMlp(
hidden_sizes=hidden_sizes,
input_size=obs_dim + action_dim,
output_size=1,
)
qf2 = FlattenMlp(
hidden_sizes=hidden_sizes,
input_size=obs_dim + action_dim,
output_size=1,
)
target_qf1 = FlattenMlp(
hidden_sizes=hidden_sizes,
input_size=obs_dim + action_dim,
output_size=1,
)
target_qf2 = FlattenMlp(
hidden_sizes=hidden_sizes,
input_size=obs_dim + action_dim,
output_size=1,
)
policy = ReparamTanhMultivariateGaussianPolicy(
# policy = ReparamMultivariateGaussianPolicy(
hidden_sizes=hidden_sizes,
obs_dim=obs_dim,
action_dim=action_dim,
# std=0.1
)
# set up the discriminator models
disc_model_class = ThreeWayResNetAIRLDisc if variant[
'threeway'] else ResNetAIRLDisc
disc_model = disc_model_class(
2, # obs is just x-y pos
num_layer_blocks=variant['disc_num_blocks'],
hid_dim=variant['disc_hid_dim'],
hid_act=variant['disc_hid_act'],
use_bn=variant['disc_use_bn'],
clamp_magnitude=variant['disc_clamp_magnitude'])
print(disc_model)
print(disc_model.clamp_magnitude)
# set up the RL algorithm used to train the policy
policy_optimizer = EntConstSAC(policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
action_dim=action_dim,
**variant['policy_params'])
# set up the AIRL algorithm
alg_class = ThreewayStateMarginalMatchingAlg if variant[
'threeway'] else StateMarginalMatchingAlg
algorithm = alg_class(env,
policy,
disc_model,
policy_optimizer,
expert_buffer,
training_env=training_env,
**variant['algo_params'])
print(algorithm.exploration_policy)
print(algorithm.eval_policy)
print(algorithm.policy_optimizer.policy_optimizer.defaults['lr'])
print(algorithm.policy_optimizer.qf1_optimizer.defaults['lr'])
print(algorithm.policy_optimizer.qf2_optimizer.defaults['lr'])
print(algorithm.disc_optimizer.defaults['lr'])
# train
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train()
return 1
def experiment(variant):
# NEW WAY OF DOING EXPERT REPLAY BUFFERS USING ExpertReplayBuffer class
with open(EXPERT_LISTING_YAML_PATH, 'r') as f:
listings = yaml.load(f.read())
print(listings.keys())
expert_dir = listings[variant['expert_name']]['exp_dir']
specific_run = listings[variant['expert_name']]['seed_runs'][variant['expert_seed_run_idx']]
file_to_load = path.join(expert_dir, specific_run, 'extra_data.pkl')
expert_replay_buffer = joblib.load(file_to_load)['replay_buffer']
# this script is for the non-meta-learning GAIL
expert_replay_buffer.policy_uses_task_params = variant['gail_params']['policy_uses_task_params']
expert_replay_buffer.concat_task_params_to_policy_obs = variant['gail_params']['concat_task_params_to_policy_obs']
# Now determine how many trajectories you want to use
if 'num_expert_trajs' in variant: raise NotImplementedError('Not implemented during the transition away from ExpertReplayBuffer')
# we have to generate the combinations for the env_specs
env_specs = variant['env_specs']
if env_specs['train_test_env']:
env, training_env = get_env(env_specs)
else:
env, _ = get_env(env_specs)
training_env, _ = get_env(env_specs)
env.seed(variant['seed'])
training_env.seed(variant['seed'])
# if variant['wrap_absorbing_state']:
# assert False, 'Not handling train_test_env'
# env = WrappedAbsorbingEnv(env)
print(env.observation_space)
if isinstance(env.observation_space, Dict):
if not variant['gail_params']['policy_uses_pixels']:
obs_dim = int(np.prod(env.observation_space.spaces['obs'].shape))
if variant['gail_params']['policy_uses_task_params']:
if variant['gail_params']['concat_task_params_to_policy_obs']:
obs_dim += int(np.prod(env.observation_space.spaces['obs_task_params'].shape))
else:
raise NotImplementedError()
else:
raise NotImplementedError()
else:
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
print(obs_dim, action_dim)
sleep(3)
if variant['gail_params']['state_only']: print('\n\nUSING STATE ONLY DISC\n\n')
disc_model = ThirdVersionSingleColorFetchCustomDisc(
clamp_magnitude=variant['disc_clamp_magnitude'],
state_only=variant['gail_params']['state_only'],
wrap_absorbing=variant['gail_params']['wrap_absorbing']
)
if variant['gail_params']['use_target_disc']:
target_disc = disc_model.copy()
else:
target_disc = None
print(disc_model)
print(disc_model.clamp_magnitude)
policy_net_size = variant['policy_net_size']
hidden_sizes = [policy_net_size] * variant['num_hidden_layers']
qf1 = ObsPreprocessedQFunc(
target_disc.obs_processor if target_disc is not None else disc_model.obs_processor,
hidden_sizes=hidden_sizes,
input_size=6 + 4 + 4 + 1*variant['gail_params']['wrap_absorbing'],
output_size=1,
wrap_absorbing=variant['gail_params']['wrap_absorbing']
)
qf2 = ObsPreprocessedQFunc(
target_disc.obs_processor if target_disc is not None else disc_model.obs_processor,
hidden_sizes=hidden_sizes,
input_size=6 + 4 + 4 + 1*variant['gail_params']['wrap_absorbing'],
output_size=1,
wrap_absorbing=variant['gail_params']['wrap_absorbing']
)
vf = ObsPreprocessedVFunc(
target_disc.obs_processor if target_disc is not None else disc_model.obs_processor,
hidden_sizes=hidden_sizes,
input_size=6 + 4 + 1*variant['gail_params']['wrap_absorbing'],
output_size=1,
wrap_absorbing=variant['gail_params']['wrap_absorbing']
)
policy = ObsPreprocessedReparamTanhMultivariateGaussianPolicy(
target_disc.obs_processor if target_disc is not None else disc_model.obs_processor,
hidden_sizes=hidden_sizes,
obs_dim=6 + 4,
action_dim=4,
)
policy_optimizer = NewSoftActorCritic(
policy=policy,
qf1=qf1,
qf2=qf2,
vf=vf,
wrap_absorbing=variant['gail_params']['wrap_absorbing'],
**variant['policy_params']
)
algorithm = GAIL(
env,
policy,
disc_model,
policy_optimizer,
expert_replay_buffer,
training_env=training_env,
target_disc=target_disc,
**variant['gail_params']
)
print(algorithm.use_target_disc)
print(algorithm.soft_target_disc_tau)
print(algorithm.exploration_policy)
print(algorithm.eval_policy)
print(algorithm.policy_optimizer.policy_optimizer.defaults['lr'])
print(algorithm.policy_optimizer.qf1_optimizer.defaults['lr'])
print(algorithm.policy_optimizer.qf2_optimizer.defaults['lr'])
print(algorithm.policy_optimizer.vf_optimizer.defaults['lr'])
print(algorithm.disc_optimizer.defaults['lr'])
if variant['gail_params']['wrap_absorbing']:
print('\n\nWRAP ABSORBING\n\n')
# assert False, "Have not added new sac yet!"
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train()
return 1
def experiment(variant):
# get the expert data
with open(EXPERT_LISTING_YAML_PATH, 'r') as f:
listings = yaml.load(f.read())
expert_dir = listings[variant['expert_name']]['exp_dir']
specific_run = listings[variant['expert_name']]['seed_runs'][
variant['expert_seed_run_idx']]
file_to_load = path.join(expert_dir, specific_run, 'extra_data.pkl')
extra_data = joblib.load(file_to_load)
expert_buffer = extra_data['train']
# set up the env
env_specs = variant['env_specs']
if env_specs['train_test_env']:
env, training_env = get_env(env_specs)
else:
env, _ = get_env(env_specs)
training_env, _ = get_env(env_specs)
if variant['scale_env_with_given_demo_stats']:
assert not env_specs['normalized']
env = ScaledEnv(
env,
obs_mean=extra_data['obs_mean'],
obs_std=extra_data['obs_std'],
acts_mean=extra_data['acts_mean'],
acts_std=extra_data['acts_std'],
)
training_env = ScaledEnv(
training_env,
obs_mean=extra_data['obs_mean'],
obs_std=extra_data['obs_std'],
acts_mean=extra_data['acts_mean'],
acts_std=extra_data['acts_std'],
)
# seed the env
env.seed(variant['seed'])
training_env.seed(variant['seed'])
print(env.observation_space)
# compute obs_dim and action_dim
if isinstance(env.observation_space, Dict):
if not variant['algo_params']['policy_uses_pixels']:
obs_dim = int(np.prod(env.observation_space.spaces['obs'].shape))
if variant['algo_params']['policy_uses_task_params']:
if variant['algo_params']['concat_task_params_to_policy_obs']:
obs_dim += int(
np.prod(env.observation_space.
spaces['obs_task_params'].shape))
else:
raise NotImplementedError()
else:
raise NotImplementedError()
else:
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
print(obs_dim, action_dim)
sleep(3)
# set up the policy models
policy_net_size = variant['policy_net_size']
hidden_sizes = [policy_net_size] * variant['policy_num_hidden_layers']
policy = ReparamTanhMultivariateGaussianPolicy(
hidden_sizes=hidden_sizes,
obs_dim=obs_dim,
action_dim=action_dim,
batch_norm=variant['policy_uses_bn'],
layer_norm=variant['policy_uses_layer_norm'])
# policy = MlpPolicy(
# hidden_sizes=hidden_sizes,
# obs_dim=obs_dim,
# action_dim=action_dim,
# batch_norm=variant['policy_uses_bn'],
# layer_norm=variant['policy_uses_layer_norm']
# )
# set up the AIRL algorithm
algorithm = BC(env,
policy,
expert_buffer,
training_env=training_env,
wrap_absorbing=variant['wrap_absorbing_state'],
**variant['algo_params'])
print(algorithm.exploration_policy)
print(algorithm.eval_policy)
# train
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train()
return 1
def experiment(variant):
env_specs = variant['env_specs']
if variant['algo_params']['meta']:
env, training_env = get_meta_env(env_specs)
else:
if env_specs['train_test_env']:
env, training_env = get_env(env_specs)
else:
env, _ = get_env(env_specs)
training_env, _ = get_env(env_specs)
if variant['algo_params']['meta']:
train_task_params_sampler, test_task_params_sampler = get_meta_env_params_iters(
env_specs)
print(env.observation_space)
if isinstance(env.observation_space, Dict):
if not variant['algo_params']['policy_uses_pixels']:
obs_dim = int(np.prod(env.observation_space.spaces['obs'].shape))
if variant['algo_params']['policy_uses_task_params']:
obs_dim += int(
np.prod(
env.observation_space.spaces['obs_task_params'].shape))
else:
raise NotImplementedError()
else:
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
net_size = variant['net_size']
hidden_sizes = [net_size] * variant['num_hidden_layers']
if variant['use_custom_ant_models']:
assert isinstance(env.observation_space, Dict)
print('CUSTOM ANT WITH LINEAR EMBEDDING OF THE TARGET POSITION')
qf1 = AntRandGoalCustomQFunc(
int(np.prod(
env.observation_space.spaces['obs_task_params'].shape)),
variant['goal_embed_dim'],
hidden_sizes=hidden_sizes,
input_size=int(np.prod(env.observation_space.spaces['obs'].shape))
+ action_dim,
output_size=1,
)
qf2 = AntRandGoalCustomQFunc(
int(np.prod(
env.observation_space.spaces['obs_task_params'].shape)),
variant['goal_embed_dim'],
hidden_sizes=hidden_sizes,
input_size=int(np.prod(env.observation_space.spaces['obs'].shape))
+ action_dim,
output_size=1,
)
vf = AntRandGoalCustomVFunc(
int(np.prod(
env.observation_space.spaces['obs_task_params'].shape)),
variant['goal_embed_dim'],
hidden_sizes=hidden_sizes,
input_size=int(np.prod(env.observation_space.spaces['obs'].shape)),
output_size=1,
)
policy = AntRandGoalCustomReparamTanhMultivariateGaussianPolicy(
int(np.prod(
env.observation_space.spaces['obs_task_params'].shape)),
variant['goal_embed_dim'],
hidden_sizes=hidden_sizes,
obs_dim=int(np.prod(env.observation_space.spaces['obs'].shape)),
action_dim=action_dim,
)
# CUSTOM ANT WITH GATING ACTIVATIONS OF EACH LAYER
# qf1 = AntCustomGatingQFuncV1()
# qf2 = AntCustomGatingQFuncV1()
# vf = AntCustomGatingVFuncV1()
# policy = AntCustomGatingV1ReparamTanhMultivariateGaussianPolicy()
else:
print('Using simple model')
qf1 = FlattenMlp(
hidden_sizes=hidden_sizes,
input_size=obs_dim + action_dim,
output_size=1,
)
qf2 = FlattenMlp(
hidden_sizes=hidden_sizes,
input_size=obs_dim + action_dim,
output_size=1,
)
vf = FlattenMlp(
hidden_sizes=hidden_sizes,
input_size=obs_dim,
output_size=1,
)
policy = ReparamTanhMultivariateGaussianPolicy(
hidden_sizes=hidden_sizes,
obs_dim=obs_dim,
action_dim=action_dim,
)
if variant['algo_params']['meta']:
algorithm = MetaNewSoftActorCritic(
env=env,
training_env=training_env,
policy=policy,
qf1=qf1,
qf2=qf2,
vf=vf,
train_task_params_sampler=train_task_params_sampler,
test_task_params_sampler=test_task_params_sampler,
true_env_obs_dim=int(
np.prod(env.observation_space.spaces['obs'].shape)),
**variant['algo_params'])
else:
algorithm = NewSoftActorCritic(env=env,
training_env=training_env,
policy=policy,
qf1=qf1,
qf2=qf2,
vf=vf,
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train()
return 1
def experiment(variant):
with open('expert_demos_listing.yaml', 'r') as f:
listings = yaml.load(f.read())
demos_path = listings[variant['expert_name']]['file_paths'][
variant['expert_idx']]
buffer_save_dict = joblib.load(demos_path)
target_state_buffer = buffer_save_dict['data']
target_state_buffer /= variant['rescale']
state_indices = torch.LongTensor(variant['state_indices'])
env_specs = variant['env_specs']
env = get_env(env_specs)
env.seed(env_specs['eval_env_seed'])
training_env = get_env(env_specs)
training_env.seed(env_specs['training_env_seed'])
print('\n\nEnv: {}'.format(env_specs['env_name']))
print('kwargs: {}'.format(env_specs['env_kwargs']))
print('Obs Space: {}'.format(env.observation_space))
print('Act Space: {}\n\n'.format(env.action_space))
obs_space = env.observation_space
act_space = env.action_space
assert not isinstance(obs_space, Dict)
assert len(obs_space.shape) == 1
assert len(act_space.shape) == 1
obs_dim = obs_space.shape[0]
action_dim = act_space.shape[0]
# build the policy models
net_size = variant['policy_net_size']
num_hidden = variant['policy_num_hidden_layers']
policy = ReparamTanhMultivariateGaussianPolicy(
hidden_sizes=num_hidden * [net_size],
obs_dim=obs_dim,
action_dim=action_dim,
)
input_dim = len(state_indices)
# build the energy model
if (variant['ebm_params']['mode']) == 'deen':
ebm_model = MLPEBM(input_dim,
num_layer_blocks=variant['ebm_num_blocks'],
hid_dim=variant['ebm_hid_dim'],
hid_act=variant['ebm_hid_act'],
use_bn=variant['ebm_use_bn'],
clamp_magnitude=variant['ebm_clamp_magnitude'])
algorithm = EBMLearn(env=env,
training_env=training_env,
ebm=ebm_model,
input_dim=input_dim,
exploration_policy=policy,
sigma=variant['sigma'],
target_state_buffer=target_state_buffer,
state_indices=state_indices,
**variant['ebm_params'])
# build the energy model
elif (variant['ebm_params']['mode']) == 'ae':
ebm_model = MLPAE(
input_dim,
num_layer_blocks=variant['ebm_num_blocks'],
hid_dim=variant['ebm_hid_dim'],
hid_act=variant['ebm_hid_act'],
use_bn=variant['ebm_use_bn'],
)
algorithm = EBMLearn(env=env,
training_env=training_env,
ebm=ebm_model,
input_dim=input_dim,
exploration_policy=policy,
sigma=None,
rescale=variant['rescale'],
target_state_buffer=target_state_buffer,
state_indices=state_indices,
**variant['ebm_params'])
if ptu.gpu_enabled():
algorithm.to(ptu.device)
algorithm.train()
return 1
def experiment(variant):
# env = NormalizedBoxEnv(HalfCheetahEnv())
# env = NormalizedBoxEnv(InvertedPendulumEnv())
# ---------
# env = NormalizedBoxEnv(get_meta_env(variant['env_specs']))
# training_env = NormalizedBoxEnv(get_meta_env(variant['env_specs']))
# env = ReacherEnv()
# training_env = ReacherEnv()
# env = NormalizedBoxEnv(ReacherEnv())
# training_env = NormalizedBoxEnv(ReacherEnv())
# Or for a specific version:
# import gym
# env = NormalizedBoxEnv(gym.make('HalfCheetah-v1'))
# we have to generate the combinations for the env_specs
env_specs = variant['env_specs']
env, _ = get_env(env_specs)
training_env, _ = get_env(env_specs)
print(env.observation_space)
obs_space = env.observation_space
if isinstance(env.observation_space, Dict):
# possible keys: pixel, obs, obs_task_params
if not variant['algo_params']['policy_uses_pixels']:
obs_dim = int(np.prod(obs_space.spaces['obs'].shape))
else:
raise NotImplementedError()
if variant['algo_params']['policy_uses_task_params']:
if variant['algo_params']['concat_task_params_to_policy_obs']:
obs_dim += int(
np.prod(obs_space.spaces['obs_task_params'].shape))
else:
raise NotImplementedError
else:
# OpenAI Gym Env or DMCS Env with only one obs
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
# if variant['reload_policy_from'] != '':
# params = joblib.load(variant['reload_policy_from'])
# qf1, qf2, vf, policy = params['qf1'], params['qf2'], params['vf'], params['policy']
# else:
net_size = variant['net_size']
qf1 = FlattenMlp(
hidden_sizes=[net_size, net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
qf2 = FlattenMlp(
hidden_sizes=[net_size, net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
vf = FlattenMlp(
hidden_sizes=[net_size, net_size],
input_size=obs_dim,
output_size=1,
)
policy = ReparamTanhMultivariateGaussianPolicy(
hidden_sizes=[net_size, net_size],
obs_dim=obs_dim,
action_dim=action_dim,
)
algorithm = NewSoftActorCritic(env=env,
training_env=training_env,
policy=policy,
qf1=qf1,
qf2=qf2,
vf=vf,
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train()
return 1
def experiment(specs):
if not specs['use_scripted_policy']:
policy_is_scripted = False
policy = joblib.load(specs['expert_path'])['policy']
else:
policy_is_scripted = True
policy = get_scripted_policy(specs['scripted_policy_name'])
if specs['use_deterministic_expert']:
policy = MakeDeterministic(policy)
if ptu.gpu_enabled():
policy.to(ptu.device)
env = get_env(specs['env_specs'])
env.seed(specs['env_specs']['env_seed'])
# make the replay buffers
max_path_length = specs['max_path_length']
if 'wrap_absorbing' in specs and specs['wrap_absorbing']:
"""
There was an intial implementation for this in v1.0
in gen_irl_expert_trajs.py
"""
raise NotImplementedError()
_max_buffer_size = (max_path_length + 2) * specs['num_rollouts']
else:
_max_buffer_size = max_path_length * specs['num_rollouts']
_max_buffer_size = int(
np.ceil(_max_buffer_size / float(specs['subsample_factor'])))
buffer_constructor = lambda: EnvReplayBuffer(
_max_buffer_size,
env,
)
train_buffer = buffer_constructor()
test_buffer = buffer_constructor()
render = specs['render']
render_kwargs = specs['render_kwargs']
check_for_success = specs['check_for_success']
print('\n')
# fill the train buffer
fill_buffer(train_buffer,
env,
policy,
specs['num_rollouts'],
max_path_length,
no_terminal=specs['no_terminal'],
policy_is_scripted=policy_is_scripted,
render=render,
render_kwargs=render_kwargs,
check_for_success=check_for_success,
wrap_absorbing=False,
subsample_factor=specs['subsample_factor'])
# fill the test buffer
fill_buffer(test_buffer,
env,
policy,
specs['num_rollouts'],
max_path_length,
no_terminal=specs['no_terminal'],
policy_is_scripted=policy_is_scripted,
render=render,
render_kwargs=render_kwargs,
check_for_success=check_for_success,
wrap_absorbing=False,
subsample_factor=specs['subsample_factor'])
# save the replay buffers
logger.save_extra_data({
'train': train_buffer,
'test': test_buffer
},
name='expert_demos.pkl')
return 1
def experiment(variant):
env_specs = variant['env_specs']
env = get_env(env_specs)
env.seed(env_specs['eval_env_seed'])
training_env = get_env(env_specs)
training_env.seed(env_specs['training_env_seed'])
print('\n\nEnv: {}'.format(env_specs['env_name']))
print('kwargs: {}'.format(env_specs['env_kwargs']))
print('Obs Space: {}'.format(env.observation_space))
print('Act Space: {}\n\n'.format(env.action_space))
obs_space = env.observation_space
act_space = env.action_space
assert not isinstance(obs_space, Dict)
assert len(obs_space.shape) == 1
assert len(act_space.shape) == 1
obs_dim = obs_space.shape[0]
action_dim = act_space.shape[0]
# build the energy model
if variant['ebil_params']['mode'] == 'deen':
"""
ebm_model = MLPEBM(
obs_dim + action_dim if not variant['ebil_params']['state_only'] else 2*obs_dim,
num_layer_blocks=variant['ebm_num_blocks'],
hid_dim=variant['ebm_hid_dim'],
hid_act=variant['ebm_hid_act'],
use_bn=variant['ebm_use_bn'],
clamp_magnitude=variant['ebm_clamp_magnitude'],
)
"""
ebm_exp_name = 'ebm-deen-smm-implementation-' + variant['env_specs'][
'task_name']
ebm_dir = os.path.join(config.LOCAL_LOG_DIR, ebm_exp_name)
ebm_id_dirs = os.listdir(ebm_dir)
tmp = []
ebm_id_dic = ebm_id_dics[variant['env_specs']['env_name'] + '_' +
variant['env_specs']['task_name']]
if str(variant['ebm_sigma']) in ebm_id_dic['sigma'].keys():
ebm_id = ebm_id_dic['sigma'][str(variant['ebm_sigma'])]
tmp = [_ for _ in ebm_id_dirs if ebm_id in _]
else:
raise NotImplementedError
if len(tmp) > 0:
ebm_id_dirs = tmp
ebm_id_dirs = sorted(
ebm_id_dirs,
key=lambda x: os.path.getmtime(os.path.join(ebm_dir, x)))
load_ebm_dir = os.path.join(
ebm_dir, ebm_id_dirs[-1]) # Choose the last as the load ebm dir
load_epoch = variant['ebm_epoch']
load_name = 'itr_{}.pkl'.format(load_epoch)
if load_epoch == 'best':
load_name = 'best.pkl'
load_ebm_path = os.path.join(load_ebm_dir, load_name)
load_ebm_pkl = joblib.load(load_ebm_path, mmap_mode='r')
ebm_model = load_ebm_pkl['ebm']
print("loaded EBM from {}".format(load_ebm_path))
elif variant['ebil_params']['mode'] == 'ae':
ebm_exp_name = 'ebm-ae-' + variant['env_specs'][
'env_name'] + '-' + str(variant['expert_traj_num']) + '-train'
ebm_dir = os.path.join(config.LOCAL_LOG_DIR, ebm_exp_name)
ebm_id_dirs = os.listdir(ebm_dir)
ebm_id_dirs = sorted(
ebm_id_dirs,
key=lambda x: os.path.getmtime(os.path.join(ebm_dir, x)))
load_ebm_dir = os.path.join(
ebm_dir, ebm_id_dirs[-1]) # Choose the last as the load ebm dir
load_epoch = variant['ebm_epoch']
load_name = 'itr_{}.pkl'.format(load_epoch)
if load_epoch == 'best':
load_name = 'best.pkl'
load_ebm_path = os.path.join(load_ebm_dir, load_name)
load_ebm_pkl = joblib.load(load_ebm_path, mmap_mode='r')
ebm_model = load_ebm_pkl['ebm']
print("loaded EBM from {}".format(load_ebm_path))
else:
raise NotImplementedError
# Test
if variant['test']:
batch_data = target_state_buffer
obs = torch.Tensor(batch_data[:100])
exp_input = torch.cat([obs, acts], dim=1).to(ptu.device)
print("Not expert data", ebm_model(exp_input * 200).mean().item())
print("Expert data", ebm_model(exp_input).mean().item())
exit(1)
x = np.linspace(-1.25, 20, 1000)
y = np.linspace(-1.25, 10, 1000)
rewards = []
for i in range(1000):
data = []
for j in range(1000):
coords = np.array((x[j], y[i]))
data.append((x[j], y[i]))
data = np.array(data) / variant['rescale']
data = torch.Tensor(data).to(ptu.device)
reward = ebm_model(data).squeeze().detach().cpu().numpy()
rewards.append(reward)
#data = np.array(data)
#data = torch.Tensor(data).to(ptu.device)
rewards = np.array(rewards)
print(rewards.shape)
# rewards = np.reshape(rewards, (1000,1000))
fig, ax = plt.subplots(figsize=(6, 6))
# im = ax.imshow(rewards, cmap=plt.cm.hot_r)
# plt.colorbar(im)
h = plt.contourf(rewards, cmap=plt.cm.hot_r)
cb = plt.colorbar(h)
ax.set_xticks([58, 293, 528, 764, 999])
ax.set_xticklabels(['0', '5', '10', '15', '20'])
ax.set_yticks([111, 555, 999])
ax.set_yticklabels(['0', '5', '10'])
plt.savefig('./figs/' + variant['env_specs']['env_name'] + '_' +
variant['env_specs']['task_name'] + '_' +
str(variant['ebm_sigma']) + '_' + str(variant['ebm_epoch']) +
'.pdf',
bbox_inches='tight')
return 1
from os import path as osp
import yaml
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import rlkit.torch.pytorch_util as ptu
from rlkit.envs import get_env
from rlkit.core import eval_util
from rlkit.samplers import PathSampler
from rlkit.torch.sac.policies import MakeDeterministic
from rlkit.envs.wrappers import ScaledEnv
env_specs = {'env_name': 'halfcheetah', 'env_kwargs': {}, 'eval_env_seed': 3562}
env = get_env(env_specs)
env.seed(env_specs['eval_env_seed'])
with open('expert_demos_listing.yaml', 'r') as f:
listings = yaml.load(f.read())
expert_demos_path = listings['norm_halfcheetah_32_demos_sub_20']['file_paths'][0]
buffer_save_dict = joblib.load(expert_demos_path)
env = ScaledEnv(
env,
obs_mean=buffer_save_dict['obs_mean'],
obs_std=buffer_save_dict['obs_std'],
acts_mean=buffer_save_dict['acts_mean'],
acts_std=buffer_save_dict['acts_std'],
)
bc_policy = joblib.load('/scratch/hdd001/home/kamyar/output/paper-version-hc-bc/paper_version_hc_bc_2019_05_19_00_32_05_0000--s-0/params.pkl')['exploration_policy']
bc_policy = MakeDeterministic(bc_policy)
def experiment(variant):
env_specs = variant['env_specs']
env = get_env(env_specs)
env.seed(env_specs['eval_env_seed'])
training_env = get_env(env_specs)
training_env.seed(env_specs['training_env_seed'])
print('\n\nEnv: {}'.format(env_specs['env_name']))
print('kwargs: {}'.format(env_specs['env_kwargs']))
print('Obs Space: {}'.format(env.observation_space))
print('Act Space: {}\n\n'.format(env.action_space))
obs_space = env.observation_space
act_space = env.action_space
assert not isinstance(obs_space, Dict)
assert len(obs_space.shape) == 1
assert len(act_space.shape) == 1
obs_dim = obs_space.shape[0]
action_dim = act_space.shape[0]
net_size = variant['net_size']
num_hidden = variant['num_hidden_layers']
qf1 = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
qf2 = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
vf = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim,
output_size=1,
)
policy = ReparamTanhMultivariateGaussianPolicy(
hidden_sizes=num_hidden * [net_size],
obs_dim=obs_dim,
action_dim=action_dim,
)
trainer = SoftActorCritic(
policy=policy,
qf1=qf1,
qf2=qf2,
vf=vf,
**variant['sac_params']
)
algorithm = TorchRLAlgorithm(
trainer=trainer,
env=env,
training_env=training_env,
exploration_policy=policy,
**variant['rl_alg_params']
)
if ptu.gpu_enabled():
algorithm.to(ptu.device)
algorithm.train()
return 1
def experiment(variant):
with open('expert_demos_listing.yaml', 'r') as f:
listings = yaml.load(f.read())
demos_path = listings[variant['expert_name']]['file_paths'][
variant['expert_idx']]
buffer_save_dict = joblib.load(demos_path)
target_state_buffer = buffer_save_dict['data']
state_indices = torch.LongTensor(variant['state_indices'])
env_specs = variant['env_specs']
env = get_env(env_specs)
env.seed(env_specs['eval_env_seed'])
training_env = get_env(env_specs)
training_env.seed(env_specs['training_env_seed'])
print('\n\nEnv: {}'.format(env_specs['env_name']))
print('kwargs: {}'.format(env_specs['env_kwargs']))
print('Obs Space: {}'.format(env.observation_space))
print('Act Space: {}\n\n'.format(env.action_space))
obs_space = env.observation_space
act_space = env.action_space
assert not isinstance(obs_space, Dict)
assert len(obs_space.shape) == 1
assert len(act_space.shape) == 1
obs_dim = obs_space.shape[0]
action_dim = act_space.shape[0]
# build the policy models
net_size = variant['policy_net_size']
num_hidden = variant['policy_num_hidden_layers']
qf1 = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
qf2 = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
vf = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim,
output_size=1,
)
policy = ReparamTanhMultivariateGaussianPolicy(
hidden_sizes=num_hidden * [net_size],
obs_dim=obs_dim,
action_dim=action_dim,
)
# build the discriminator model
if variant['disc_model_type'] == 'resnet_disc':
disc_model = ResNetAIRLDisc(
len(state_indices),
num_layer_blocks=variant['disc_num_blocks'],
hid_dim=variant['disc_hid_dim'],
hid_act=variant['disc_hid_act'],
use_bn=variant['disc_use_bn'],
clamp_magnitude=variant['disc_clamp_magnitude'])
else:
disc_model = MLPDisc(len(state_indices),
num_layer_blocks=variant['disc_num_blocks'],
hid_dim=variant['disc_hid_dim'],
hid_act=variant['disc_hid_act'],
use_bn=variant['disc_use_bn'],
clamp_magnitude=variant['disc_clamp_magnitude'])
# set up the algorithm
trainer = SoftActorCritic(policy=policy,
qf1=qf1,
qf2=qf2,
vf=vf,
**variant['sac_params'])
algorithm = AdvSMM(env=env,
training_env=training_env,
exploration_policy=policy,
discriminator=disc_model,
policy_trainer=trainer,
target_state_buffer=target_state_buffer,
state_indices=state_indices,
**variant['adv_smm_params'])
if ptu.gpu_enabled():
algorithm.to(ptu.device)
algorithm.train()
return 1
def experiment(variant):
with open('expert_demos_listing.yaml', 'r') as f:
listings = yaml.load(f.read())
expert_demos_path = listings[variant['expert_name']]['file_paths'][
variant['expert_idx']]
buffer_save_dict = joblib.load(expert_demos_path)
expert_replay_buffer = buffer_save_dict['train']
if 'minmax_env_with_demo_stats' in variant.keys():
if variant['minmax_env_with_demo_stats']:
print('Use minmax envs')
assert 'norm_train' in buffer_save_dict.keys()
expert_replay_buffer = buffer_save_dict['norm_train']
env_specs = variant['env_specs']
env = get_env(env_specs)
env.seed(env_specs['eval_env_seed'])
training_env = get_env(env_specs)
training_env.seed(env_specs['training_env_seed'])
print('\n\nEnv: {}'.format(env_specs['env_name']))
print('kwargs: {}'.format(env_specs['env_kwargs']))
print('Obs Space: {}'.format(env.observation_space))
print('Act Space: {}\n\n'.format(env.action_space))
if variant['scale_env_with_demo_stats']:
env = ScaledEnv(
env,
obs_mean=buffer_save_dict['obs_mean'],
obs_std=buffer_save_dict['obs_std'],
acts_mean=buffer_save_dict['acts_mean'],
acts_std=buffer_save_dict['acts_std'],
)
training_env = ScaledEnv(
training_env,
obs_mean=buffer_save_dict['obs_mean'],
obs_std=buffer_save_dict['obs_std'],
acts_mean=buffer_save_dict['acts_mean'],
acts_std=buffer_save_dict['acts_std'],
)
elif variant['minmax_env_with_demo_stats']:
env = MinmaxEnv(
env,
obs_min=buffer_save_dict['obs_min'],
obs_max=buffer_save_dict['obs_max'],
)
training_env = MinmaxEnv(
training_env,
obs_min=buffer_save_dict['obs_min'],
obs_max=buffer_save_dict['obs_max'],
)
obs_space = env.observation_space
act_space = env.action_space
assert not isinstance(obs_space, Dict)
assert len(obs_space.shape) == 1
assert len(act_space.shape) == 1
obs_dim = obs_space.shape[0]
action_dim = act_space.shape[0]
# build the policy models
net_size = variant['policy_net_size']
num_hidden = variant['policy_num_hidden_layers']
qf1 = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
qf2 = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
vf = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim,
output_size=1,
)
policy = ReparamTanhMultivariateGaussianPolicy(
hidden_sizes=num_hidden * [net_size],
obs_dim=obs_dim,
action_dim=action_dim,
)
# build the energy model
if variant['ebil_params']['mode'] == 'deen':
"""
ebm_model = MLPEBM(
obs_dim + action_dim if not variant['ebil_params']['state_only'] else 2*obs_dim,
num_layer_blocks=variant['ebm_num_blocks'],
hid_dim=variant['ebm_hid_dim'],
hid_act=variant['ebm_hid_act'],
use_bn=variant['ebm_use_bn'],
clamp_magnitude=variant['ebm_clamp_magnitude'],
)
"""
ebm_exp_name = 'ebm-deen-' + variant['env_specs'][
'env_name'] + '-' + str(
variant['expert_traj_num']) + '-train--sigma-' + str(
variant['ebm_sigma'])
ebm_dir = os.path.join(config.LOCAL_LOG_DIR, ebm_exp_name)
ebm_id_dirs = os.listdir(ebm_dir)
ebm_id_dirs = sorted(
ebm_id_dirs,
key=lambda x: os.path.getmtime(os.path.join(ebm_dir, x)))
load_ebm_dir = os.path.join(
ebm_dir, ebm_id_dirs[-1]) # Choose the last as the load ebm dir
load_epoch = variant['ebm_epoch']
load_name = 'itr_{}.pkl'.format(load_epoch)
if load_epoch == 'best':
load_name = 'best.pkl'
load_ebm_path = os.path.join(load_ebm_dir, load_name)
load_ebm_pkl = joblib.load(load_ebm_path, mmap_mode='r')
ebm_model = load_ebm_pkl['ebm']
elif variant['ebil_params']['mode'] == 'ae':
ebm_exp_name = 'ebm-ae-' + variant['env_specs']['env_name'] + '-' + str(
variant['expert_traj_num']) + '-train--sigma-' + str(
variant['ebm_sigma'])
ebm_dir = os.path.join(config.LOCAL_LOG_DIR, ebm_exp_name)
ebm_id_dirs = os.listdir(ebm_dir)
ebm_id_dirs = sorted(
ebm_id_dirs,
key=lambda x: os.path.getmtime(os.path.join(ebm_dir, x)))
load_ebm_dir = os.path.join(
ebm_dir, ebm_id_dirs[-1]) # Choose the last as the load ebm dir
load_epoch = variant['ebm_epoch']
load_name = 'itr_{}.pkl'.format(load_epoch)
if load_epoch == 'best':
load_name = 'best.pkl'
load_ebm_path = os.path.join(load_ebm_dir, load_name)
load_ebm_pkl = joblib.load(load_ebm_path, mmap_mode='r')
ebm_model = load_ebm_pkl['ebm']
else:
raise NotImplementedError
print("loaded EBM from {}".format(load_ebm_path))
# Test
if variant['test']:
batch_data = expert_replay_buffer.random_batch(
100, keys=['observations', 'actions'])
print('ebm_obs: ', np.mean(batch_data['observations'], axis=0))
obs = torch.Tensor(batch_data['observations'])
acts = torch.Tensor(batch_data['actions'])
exp_input = torch.cat([obs, acts], dim=1).to(ptu.device)
print("Not expert data", ebm_model(exp_input * 200).mean().item())
print("Expert data", ebm_model(exp_input).mean().item())
exit(1)
# set up the algorithm
trainer = SoftActorCritic(policy=policy,
qf1=qf1,
qf2=qf2,
vf=vf,
**variant['sac_params'])
algorithm_pretrain = BC(env=env,
training_env=training_env,
exploration_policy=policy,
expert_replay_buffer=expert_replay_buffer,
**variant['bc_params'])
algorithm = EBIL(env=env,
training_env=training_env,
exploration_policy=policy,
rew_func=variant['rew_func'],
cons=variant['cons'],
ebm=ebm_model,
policy_trainer=trainer,
expert_replay_buffer=expert_replay_buffer,
**variant['ebil_params'])
if ptu.gpu_enabled():
algorithm_pretrain.to(ptu.device)
algorithm.to(ptu.device)
else:
algorithm_pretrain.to('cpu')
algorithm.to('cpu')
if variant['pretrain']:
algorithm_pretrain.train()
algorithm.train()
return 1
def experiment(variant):
with open('expert_demos_listing.yaml', 'r') as f:
listings = yaml.load(f.read())
expert_demos_path = listings[variant['expert_name']]['file_paths'][
variant['expert_idx']]
buffer_save_dict = joblib.load(expert_demos_path)
expert_replay_buffer = buffer_save_dict['train']
env_specs = variant['env_specs']
env = get_env(env_specs)
env.seed(env_specs['eval_env_seed'])
training_env = get_env(env_specs)
training_env.seed(env_specs['training_env_seed'])
print('\n\nEnv: {}'.format(env_specs['env_name']))
print('kwargs: {}'.format(env_specs['env_kwargs']))
print('Obs Space: {}'.format(env.observation_space))
print('Act Space: {}\n\n'.format(env.action_space))
if variant['scale_env_with_demo_stats']:
env = ScaledEnv(
env,
obs_mean=buffer_save_dict['obs_mean'],
obs_std=buffer_save_dict['obs_std'],
acts_mean=buffer_save_dict['acts_mean'],
acts_std=buffer_save_dict['acts_std'],
)
training_env = ScaledEnv(
training_env,
obs_mean=buffer_save_dict['obs_mean'],
obs_std=buffer_save_dict['obs_std'],
acts_mean=buffer_save_dict['acts_mean'],
acts_std=buffer_save_dict['acts_std'],
)
obs_space = env.observation_space
act_space = env.action_space
assert not isinstance(obs_space, Dict)
assert len(obs_space.shape) == 1
assert len(act_space.shape) == 1
obs_dim = obs_space.shape[0]
action_dim = act_space.shape[0]
# build the policy models
net_size = variant['policy_net_size']
num_hidden = variant['policy_num_hidden_layers']
qf1 = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
qf2 = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim + action_dim,
output_size=1,
)
vf = FlattenMlp(
hidden_sizes=num_hidden * [net_size],
input_size=obs_dim,
output_size=1,
)
policy = ReparamTanhMultivariateGaussianPolicy(
hidden_sizes=num_hidden * [net_size],
obs_dim=obs_dim,
action_dim=action_dim,
)
# build the discriminator model
disc_model = MLPDisc(
obs_dim +
action_dim if not variant['adv_irl_params']['state_only'] else 2 *
obs_dim,
num_layer_blocks=variant['disc_num_blocks'],
hid_dim=variant['disc_hid_dim'],
hid_act=variant['disc_hid_act'],
use_bn=variant['disc_use_bn'],
clamp_magnitude=variant['disc_clamp_magnitude'])
# set up the algorithm
trainer = SoftActorCritic(policy=policy,
qf1=qf1,
qf2=qf2,
vf=vf,
**variant['sac_params'])
algorithm = AdvIRL(env=env,
training_env=training_env,
exploration_policy=policy,
discriminator=disc_model,
policy_trainer=trainer,
expert_replay_buffer=expert_replay_buffer,
**variant['adv_irl_params'])
if ptu.gpu_enabled():
algorithm.to(ptu.device)
algorithm.train()
return 1
