def experiment(variant):
expl_env = gym.make('carla-lane-dict-v0')
eval_env = expl_env
num_channels, img_width, img_height = eval_env.image_shape
num_channels = 3
action_dim = int(np.prod(eval_env.action_space.shape))
cnn_params = variant['cnn_params']
cnn_params.update(
input_width=img_width,
input_height=img_height,
input_channels=num_channels,
added_fc_input_size=0,
output_conv_channels=True,
output_size=None,
)
qf_cnn = CNN(**cnn_params)
qf_obs_processor = nn.Sequential(
qf_cnn,
Flatten(),
)
qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
qf_kwargs['obs_processor'] = qf_obs_processor
qf_kwargs['output_size'] = 1
qf_kwargs['input_size'] = (
action_dim + qf_cnn.conv_output_flat_size
)
qf1 = MlpQfWithObsProcessor(**qf_kwargs)
qf2 = MlpQfWithObsProcessor(**qf_kwargs)
target_qf_cnn = CNN(**cnn_params)
target_qf_obs_processor = nn.Sequential(
target_qf_cnn,
Flatten(),
)
target_qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
target_qf_kwargs['obs_processor'] = target_qf_obs_processor
target_qf_kwargs['output_size'] = 1
target_qf_kwargs['input_size'] = (
action_dim + target_qf_cnn.conv_output_flat_size
)
target_qf1 = MlpQfWithObsProcessor(**target_qf_kwargs)
target_qf2 = MlpQfWithObsProcessor(**target_qf_kwargs)
action_dim = int(np.prod(eval_env.action_space.shape))
policy_cnn = CNN(**cnn_params)
policy_obs_processor = nn.Sequential(
policy_cnn,
Flatten(),
)
policy = TanhGaussianPolicyAdapter(
policy_obs_processor,
policy_cnn.conv_output_flat_size,
action_dim,
**variant['policy_kwargs']
)
cnn_vae_params = variant['cnn_vae_params']
cnn_vae_params['conv_args'].update(
input_width=img_width,
input_height=img_height,
input_channels=num_channels,
)
vae_policy = ConvVAEPolicy(
representation_size=cnn_vae_params['representation_size'],
architecture=cnn_vae_params,
action_dim=action_dim,
input_channels=3,
imsize=img_width,
)
observation_key = 'image'
eval_path_collector = CustomObsDictPathCollector(
eval_env,
observation_key=observation_key,
**variant['eval_path_collector_kwargs']
)
vae_eval_path_collector = CustomObsDictPathCollector(
eval_env,
# eval_policy,
observation_key=observation_key,
**variant['eval_path_collector_kwargs']
)
#with open(variant['buffer'], 'rb') as f:
# replay_buffer = pickle.load(f)
observation_key = 'image'
replay_buffer = ObsDictReplayBuffer(
variant['replay_buffer_size'],
expl_env,
observation_key=observation_key,
)
load_hdf5(expl_env, replay_buffer)
trainer = BEARTrainer(
env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
vae=vae_policy,
**variant['trainer_kwargs']
)
expl_path_collector = ObsDictPathCollector(
expl_env,
policy,
observation_key=observation_key,
**variant['expl_path_collector_kwargs']
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
vae_evaluation_data_collector=vae_eval_path_collector,
replay_buffer=replay_buffer,
q_learning_alg=True,
batch_rl=variant['batch_rl'],
**variant['algo_kwargs']
)
video_func = VideoSaveFunctionBullet(variant)
# dump_buffer_func = BufferSaveFunction(variant)
algorithm.post_train_funcs.append(video_func)
# algorithm.post_train_funcs.append(dump_buffer_func)
algorithm.to(ptu.device)
algorithm.train()
def _pointmass_fixed_goal_experiment(vae_latent_size,
replay_buffer_size,
cnn_kwargs,
vae_kwargs,
policy_kwargs,
qf_kwargs,
e2e_trainer_kwargs,
sac_trainer_kwargs,
algorithm_kwargs,
eval_path_collector_kwargs=None,
expl_path_collector_kwargs=None,
**kwargs):
if expl_path_collector_kwargs is None:
expl_path_collector_kwargs = {}
if eval_path_collector_kwargs is None:
eval_path_collector_kwargs = {}
from multiworld.core.image_env import ImageEnv
from multiworld.envs.pygame.point2d import Point2DEnv
from multiworld.core.flat_goal_env import FlatGoalEnv
env = Point2DEnv(
images_are_rgb=True,
render_onscreen=False,
show_goal=False,
ball_radius=2,
render_size=48,
fixed_goal=(0, 0),
)
env = ImageEnv(env, imsize=env.render_size, transpose=True, normalize=True)
env = FlatGoalEnv(env) #, append_goal_to_obs=True)
input_width, input_height = env.image_shape
action_dim = int(np.prod(env.action_space.shape))
vae = ConvVAE(
representation_size=vae_latent_size,
input_channels=3,
imsize=input_width,
decoder_output_activation=nn.Sigmoid(),
# decoder_distribution='gaussian_identity_variance',
**vae_kwargs)
encoder = Vae2Encoder(vae)
def make_cnn():
return networks.CNN(input_width=input_width,
input_height=input_height,
input_channels=3,
output_conv_channels=True,
output_size=None,
**cnn_kwargs)
def make_qf():
return networks.MlpQfWithObsProcessor(obs_processor=nn.Sequential(
encoder,
networks.Flatten(),
),
output_size=1,
input_size=action_dim +
vae_latent_size,
**qf_kwargs)
qf1 = make_qf()
qf2 = make_qf()
target_qf1 = make_qf()
target_qf2 = make_qf()
action_dim = int(np.prod(env.action_space.shape))
policy_cnn = make_cnn()
policy = TanhGaussianPolicyAdapter(
nn.Sequential(policy_cnn, networks.Flatten()),
policy_cnn.conv_output_flat_size, action_dim, **policy_kwargs)
eval_env = expl_env = env
eval_policy = MakeDeterministic(policy)
eval_path_collector = MdpPathCollector(eval_env, eval_policy,
**eval_path_collector_kwargs)
replay_buffer = EnvReplayBuffer(
replay_buffer_size,
expl_env,
)
vae_trainer = VAETrainer(vae)
sac_trainer = SACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**sac_trainer_kwargs)
trainer = End2EndSACTrainer(
sac_trainer=sac_trainer,
vae_trainer=vae_trainer,
**e2e_trainer_kwargs,
)
expl_path_collector = MdpPathCollector(expl_env, policy,
**expl_path_collector_kwargs)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**algorithm_kwargs)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
import multiworld.envs.pygame
env = gym.make('Point2DEnv-ImageFixedGoal-v0')
input_width, input_height = env.image_shape
action_dim = int(np.prod(env.action_space.shape))
cnn_params = variant['cnn_params']
cnn_params.update(
input_width=input_width,
input_height=input_height,
input_channels=3,
output_conv_channels=True,
output_size=None,
)
if variant['shared_qf_conv']:
qf_cnn = PretrainedCNN(**cnn_params)
qf1 = MlpQfWithObsProcessor(
obs_processor=nn.Sequential(qf_cnn, Flatten()),
output_size=1,
input_size=action_dim + qf_cnn.conv_output_flat_size,
**variant['qf_kwargs'])
qf2 = MlpQfWithObsProcessor(
obs_processor=nn.Sequential(qf_cnn, Flatten()),
output_size=1,
input_size=action_dim + qf_cnn.conv_output_flat_size,
**variant['qf_kwargs'])
target_qf_cnn = PretrainedCNN(**cnn_params)
target_qf1 = MlpQfWithObsProcessor(
obs_processor=nn.Sequential(target_qf_cnn, Flatten()),
output_size=1,
input_size=action_dim + target_qf_cnn.conv_output_flat_size,
**variant['qf_kwargs'])
target_qf2 = MlpQfWithObsProcessor(
obs_processor=nn.Sequential(target_qf_cnn, Flatten()),
output_size=1,
input_size=action_dim + target_qf_cnn.conv_output_flat_size,
**variant['qf_kwargs'])
else:
qf1_cnn = PretrainedCNN(**cnn_params)
cnn_output_dim = qf1_cnn.conv_output_flat_size
qf1 = MlpQfWithObsProcessor(obs_processor=nn.Sequential(
qf1_cnn, Flatten()),
output_size=1,
input_size=action_dim + cnn_output_dim,
**variant['qf_kwargs'])
qf2 = MlpQfWithObsProcessor(obs_processor=nn.Sequential(
PretrainedCNN(**cnn_params), Flatten()),
output_size=1,
input_size=action_dim + cnn_output_dim,
**variant['qf_kwargs'])
target_qf1 = MlpQfWithObsProcessor(
obs_processor=nn.Sequential(PretrainedCNN(**cnn_params),
Flatten()),
output_size=1,
input_size=action_dim + cnn_output_dim,
**variant['qf_kwargs'])
target_qf2 = MlpQfWithObsProcessor(
obs_processor=nn.Sequential(PretrainedCNN(**cnn_params),
Flatten()),
output_size=1,
input_size=action_dim + cnn_output_dim,
**variant['qf_kwargs'])
action_dim = int(np.prod(env.action_space.shape))
policy_cnn = PretrainedCNN(**cnn_params)
policy = TanhGaussianPolicyAdapter(nn.Sequential(policy_cnn, Flatten()),
policy_cnn.conv_output_flat_size,
action_dim, **variant['policy_kwargs'])
eval_env = expl_env = env
eval_policy = MakeDeterministic(policy)
eval_path_collector = MdpPathCollector(
eval_env, eval_policy, **variant['eval_path_collector_kwargs'])
replay_buffer = EnvReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
trainer = SACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['trainer_kwargs'])
if variant['collection_mode'] == 'batch':
expl_path_collector = MdpPathCollector(
expl_env, policy, **variant['expl_path_collector_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
elif variant['collection_mode'] == 'online':
expl_path_collector = MdpStepCollector(
expl_env, policy, **variant['expl_path_collector_kwargs'])
algorithm = TorchOnlineRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
#expl_env = carla_env.CarlaObsDictEnv(args=variant['env_args'])
import gym
import d4rl.carla
expl_env = gym.make('carla-lane-dict-v0')
eval_env = expl_env
#num_channels, img_width, img_height = eval_env._wrapped_env.image_shape
num_channels, img_width, img_height = eval_env.image_shape
# num_channels = 3
action_dim = int(np.prod(eval_env.action_space.shape))
# obs_dim = 11
cnn_params = variant['cnn_params']
cnn_params.update(
input_width=img_width,
input_height=img_height,
input_channels=num_channels,
added_fc_input_size=0,
output_conv_channels=True,
output_size=None,
)
qf_cnn = CNN(**cnn_params)
qf_obs_processor = nn.Sequential(
qf_cnn,
Flatten(),
)
qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
qf_kwargs['obs_processor'] = qf_obs_processor
qf_kwargs['output_size'] = 1
qf_kwargs['input_size'] = (action_dim + qf_cnn.conv_output_flat_size)
qf1 = MlpQfWithObsProcessor(**qf_kwargs)
qf2 = MlpQfWithObsProcessor(**qf_kwargs)
target_qf_cnn = CNN(**cnn_params)
target_qf_obs_processor = nn.Sequential(
target_qf_cnn,
Flatten(),
)
target_qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
target_qf_kwargs['obs_processor'] = target_qf_obs_processor
target_qf_kwargs['output_size'] = 1
target_qf_kwargs['input_size'] = (action_dim +
target_qf_cnn.conv_output_flat_size)
target_qf1 = MlpQfWithObsProcessor(**target_qf_kwargs)
target_qf2 = MlpQfWithObsProcessor(**target_qf_kwargs)
action_dim = int(np.prod(eval_env.action_space.shape))
policy_cnn = CNN(**cnn_params)
policy_obs_processor = nn.Sequential(
policy_cnn,
Flatten(),
)
policy = TanhGaussianPolicyAdapter(policy_obs_processor,
policy_cnn.conv_output_flat_size,
action_dim, **variant['policy_kwargs'])
eval_policy = MakeDeterministic(policy)
observation_key = 'image'
eval_path_collector = ObsDictPathCollector(
eval_env,
eval_policy,
observation_key=observation_key,
**variant['eval_path_collector_kwargs'])
expl_path_collector = CustomObsDictPathCollector(
expl_env,
observation_key=observation_key,
)
observation_key = 'image'
replay_buffer = ObsDictReplayBuffer(
variant['replay_buffer_size'],
expl_env,
observation_key=observation_key,
)
load_hdf5(expl_env, replay_buffer)
#load_buffer(buffer_path=variant['buffer'], replay_buffer=replay_buffer)
# import ipdb; ipdb.set_trace()
trainer = SACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
behavior_policy=None,
**variant['trainer_kwargs'])
variant['algo_kwargs']['max_path_length'] = expl_env._max_episode_steps
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
eval_both=True,
batch_rl=True,
**variant['algorithm_kwargs'])
video_func = VideoSaveFunctionBullet(variant)
algorithm.post_train_funcs.append(video_func)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
expl_env = roboverse.make(variant['env'],
gui=False,
randomize=variant['randomize_env'],
observation_mode=variant['obs'],
reward_type='shaped',
transpose_image=True)
if variant['obs'] == 'pixels_debug':
robot_state_dims = 11
elif variant['obs'] == 'pixels':
robot_state_dims = 4
else:
raise NotImplementedError
expl_env = FlatEnv(expl_env,
use_robot_state=variant['use_robot_state'],
robot_state_dims=robot_state_dims)
eval_env = expl_env
img_width, img_height = eval_env.image_shape
num_channels = 3
action_dim = int(np.prod(eval_env.action_space.shape))
cnn_params = variant['cnn_params']
cnn_params.update(
input_width=img_width,
input_height=img_height,
input_channels=num_channels,
)
if variant['use_robot_state']:
cnn_params.update(
added_fc_input_size=robot_state_dims,
output_conv_channels=False,
hidden_sizes=[400, 400],
output_size=200,
)
else:
cnn_params.update(
added_fc_input_size=0,
output_conv_channels=True,
output_size=None,
)
qf_cnn = CNN(**cnn_params)
if variant['use_robot_state']:
qf_obs_processor = qf_cnn
else:
qf_obs_processor = nn.Sequential(
qf_cnn,
Flatten(),
)
qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
qf_kwargs['obs_processor'] = qf_obs_processor
qf_kwargs['output_size'] = 1
if variant['use_robot_state']:
qf_kwargs['input_size'] = (action_dim + qf_cnn.output_size)
else:
qf_kwargs['input_size'] = (action_dim + qf_cnn.conv_output_flat_size)
qf1 = MlpQfWithObsProcessor(**qf_kwargs)
qf2 = MlpQfWithObsProcessor(**qf_kwargs)
target_qf_cnn = CNN(**cnn_params)
if variant['use_robot_state']:
target_qf_obs_processor = target_qf_cnn
else:
target_qf_obs_processor = nn.Sequential(
target_qf_cnn,
Flatten(),
)
target_qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
target_qf_kwargs['obs_processor'] = target_qf_obs_processor
target_qf_kwargs['output_size'] = 1
if variant['use_robot_state']:
target_qf_kwargs['input_size'] = (action_dim +
target_qf_cnn.output_size)
else:
target_qf_kwargs['input_size'] = (action_dim +
target_qf_cnn.conv_output_flat_size)
target_qf1 = MlpQfWithObsProcessor(**target_qf_kwargs)
target_qf2 = MlpQfWithObsProcessor(**target_qf_kwargs)
action_dim = int(np.prod(eval_env.action_space.shape))
policy_cnn = CNN(**cnn_params)
if variant['use_robot_state']:
policy_obs_processor = policy_cnn
else:
policy_obs_processor = nn.Sequential(
policy_cnn,
Flatten(),
)
if variant['use_robot_state']:
policy = TanhGaussianPolicyAdapter(policy_obs_processor,
policy_cnn.output_size, action_dim,
**variant['policy_kwargs'])
else:
policy = TanhGaussianPolicyAdapter(policy_obs_processor,
policy_cnn.conv_output_flat_size,
action_dim,
**variant['policy_kwargs'])
eval_policy = MakeDeterministic(policy)
eval_path_collector = MdpPathCollector(
eval_env, eval_policy, **variant['eval_path_collector_kwargs'])
replay_buffer = EnvReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
trainer = SACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['trainer_kwargs'])
if variant['collection_mode'] == 'batch':
expl_path_collector = MdpPathCollector(
expl_env, policy, **variant['expl_path_collector_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
elif variant['collection_mode'] == 'online':
expl_path_collector = MdpStepCollector(
expl_env, policy, **variant['expl_path_collector_kwargs'])
algorithm = TorchOnlineRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
else:
raise NotImplementedError
video_func = VideoSaveFunctionBullet(variant)
algorithm.post_train_funcs.append(video_func)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
expl_env = FlatEnv(PointmassBaseEnv(observation_mode='pixels',
transpose_image=True),
use_robot_state=False)
eval_env = expl_env
img_width, img_height = (48, 48)
num_channels = 3
action_dim = int(np.prod(eval_env.action_space.shape))
cnn_params = variant['cnn_params']
cnn_params.update(
input_width=img_width,
input_height=img_height,
input_channels=num_channels,
added_fc_input_size=4,
output_conv_channels=True,
output_size=None,
)
qf_cnn = CNN(**cnn_params)
qf_obs_processor = nn.Sequential(
qf_cnn,
Flatten(),
)
qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
qf_kwargs['obs_processor'] = qf_obs_processor
qf_kwargs['output_size'] = 1
qf_kwargs['input_size'] = (action_dim + qf_cnn.conv_output_flat_size)
qf1 = MlpQfWithObsProcessor(**qf_kwargs)
qf2 = MlpQfWithObsProcessor(**qf_kwargs)
target_qf_cnn = CNN(**cnn_params)
target_qf_obs_processor = nn.Sequential(
target_qf_cnn,
Flatten(),
)
target_qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
target_qf_kwargs['obs_processor'] = target_qf_obs_processor
target_qf_kwargs['output_size'] = 1
target_qf_kwargs['input_size'] = (action_dim +
target_qf_cnn.conv_output_flat_size)
target_qf1 = MlpQfWithObsProcessor(**target_qf_kwargs)
target_qf2 = MlpQfWithObsProcessor(**target_qf_kwargs)
action_dim = int(np.prod(eval_env.action_space.shape))
policy_cnn = CNN(**cnn_params)
policy_obs_processor = nn.Sequential(
policy_cnn,
Flatten(),
)
policy = TanhGaussianPolicyAdapter(policy_obs_processor,
policy_cnn.conv_output_flat_size,
action_dim, **variant['policy_kwargs'])
eval_policy = MakeDeterministic(policy)
eval_path_collector = MdpPathCollector(
eval_env, eval_policy, **variant['eval_path_collector_kwargs'])
replay_buffer = EnvReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
trainer = SACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['trainer_kwargs'])
if variant['collection_mode'] == 'batch':
expl_path_collector = MdpPathCollector(
expl_env, policy, **variant['expl_path_collector_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
elif variant['collection_mode'] == 'online':
expl_path_collector = MdpStepCollector(
expl_env, policy, **variant['expl_path_collector_kwargs'])
algorithm = TorchOnlineRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
# from softlearning.environments.gym import register_image_reach
# register_image_reach()
# env = gym.envs.make(
# 'Pusher2d-ImageReach-v0',
# )
from softlearning.environments.gym.mujoco.image_pusher_2d import (
ImageForkReacher2dEnv)
env_kwargs = {
'image_shape': (32, 32, 3),
'arm_goal_distance_cost_coeff': 1.0,
'arm_object_distance_cost_coeff': 0.0,
}
eval_env = ImageForkReacher2dEnv(**env_kwargs)
expl_env = ImageForkReacher2dEnv(**env_kwargs)
input_width, input_height, input_channels = eval_env.image_shape
image_dim = input_width * input_height * input_channels
action_dim = int(np.prod(eval_env.action_space.shape))
cnn_params = variant['cnn_params']
cnn_params.update(
input_width=input_width,
input_height=input_height,
input_channels=input_channels,
added_fc_input_size=4,
output_conv_channels=True,
output_size=None,
)
non_image_dim = int(np.prod(eval_env.observation_space.shape)) - image_dim
if variant['shared_qf_conv']:
qf_cnn = CNN(**cnn_params)
qf_obs_processor = nn.Sequential(
Split(qf_cnn, identity, image_dim),
FlattenEach(),
Concat(),
)
qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
qf_kwargs['obs_processor'] = qf_obs_processor
qf_kwargs['output_size'] = 1
qf_kwargs['input_size'] = (action_dim + qf_cnn.conv_output_flat_size +
non_image_dim)
qf1 = MlpQfWithObsProcessor(**qf_kwargs)
qf2 = MlpQfWithObsProcessor(**qf_kwargs)
target_qf_cnn = CNN(**cnn_params)
target_qf_obs_processor = nn.Sequential(
Split(target_qf_cnn, identity, image_dim),
FlattenEach(),
Concat(),
)
target_qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
target_qf_kwargs['obs_processor'] = target_qf_obs_processor
target_qf_kwargs['output_size'] = 1
target_qf_kwargs['input_size'] = (action_dim +
target_qf_cnn.conv_output_flat_size +
non_image_dim)
target_qf1 = MlpQfWithObsProcessor(**target_qf_kwargs)
target_qf2 = MlpQfWithObsProcessor(**target_qf_kwargs)
else:
qf1_cnn = CNN(**cnn_params)
cnn_output_dim = qf1_cnn.conv_output_flat_size
qf1 = MlpQfWithObsProcessor(obs_processor=qf1_cnn,
output_size=1,
input_size=action_dim + cnn_output_dim,
**variant['qf_kwargs'])
qf2 = MlpQfWithObsProcessor(obs_processor=CNN(**cnn_params),
output_size=1,
input_size=action_dim + cnn_output_dim,
**variant['qf_kwargs'])
target_qf1 = MlpQfWithObsProcessor(obs_processor=CNN(**cnn_params),
output_size=1,
input_size=action_dim +
cnn_output_dim,
**variant['qf_kwargs'])
target_qf2 = MlpQfWithObsProcessor(obs_processor=CNN(**cnn_params),
output_size=1,
input_size=action_dim +
cnn_output_dim,
**variant['qf_kwargs'])
action_dim = int(np.prod(eval_env.action_space.shape))
policy_cnn = CNN(**cnn_params)
policy_obs_processor = nn.Sequential(
Split(policy_cnn, identity, image_dim),
FlattenEach(),
Concat(),
)
policy = TanhGaussianPolicyAdapter(
policy_obs_processor, policy_cnn.conv_output_flat_size + non_image_dim,
action_dim, **variant['policy_kwargs'])
eval_policy = MakeDeterministic(policy)
eval_path_collector = MdpPathCollector(
eval_env, eval_policy, **variant['eval_path_collector_kwargs'])
replay_buffer = EnvReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
trainer = SACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['trainer_kwargs'])
if variant['collection_mode'] == 'batch':
expl_path_collector = MdpPathCollector(
expl_env, policy, **variant['expl_path_collector_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
elif variant['collection_mode'] == 'online':
expl_path_collector = MdpStepCollector(
expl_env, policy, **variant['expl_path_collector_kwargs'])
algorithm = TorchOnlineRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
ptu.set_gpu_mode(True, 0)
from softlearning.environments.gym import register_image_reach
register_image_reach()
env = gym.make('Pusher2d-ImageReach-v0', arm_goal_distance_cost_coeff=1.0, arm_object_distance_cost_coeff=0.0)
#import ipdb; ipdb.set_trace()
input_width, input_height = env.image_shape
action_dim = int(np.prod(env.action_space.shape))
cnn_params = variant['cnn_params']
cnn_params.update(
input_width=input_width,
input_height=input_height,
input_channels=3,
added_fc_input_size=4,
output_conv_channels=True,
output_size=None,
)
if variant['shared_qf_conv']:
qf_cnn = CNN(**cnn_params)
qf1 = MlpQfWithObsProcessor(
obs_processor=qf_cnn,
output_size=1,
input_size=action_dim+qf_cnn.conv_output_flat_size,
**variant['qf_kwargs']
)
qf2 = MlpQfWithObsProcessor(
obs_processor=qf_cnn,
output_size=1,
input_size=action_dim+qf_cnn.conv_output_flat_size,
**variant['qf_kwargs']
)
target_qf_cnn = CNN(**cnn_params)
target_qf1 = MlpQfWithObsProcessor(
obs_processor=target_qf_cnn,
output_size=1,
input_size=action_dim+qf_cnn.conv_output_flat_size,
**variant['qf_kwargs']
)
target_qf2 = MlpQfWithObsProcessor(
obs_processor=target_qf_cnn,
output_size=1,
input_size=action_dim+qf_cnn.conv_output_flat_size,
**variant['qf_kwargs']
)
else:
qf1_cnn = CNN(**cnn_params)
cnn_output_dim = qf1_cnn.conv_output_flat_size
qf1 = MlpQfWithObsProcessor(
obs_processor=qf1_cnn,
output_size=1,
input_size=action_dim+cnn_output_dim,
**variant['qf_kwargs']
)
qf2 = MlpQfWithObsProcessor(
obs_processor=CNN(**cnn_params),
output_size=1,
input_size=action_dim+cnn_output_dim,
**variant['qf_kwargs']
)
target_qf1 = MlpQfWithObsProcessor(
obs_processor=CNN(**cnn_params),
output_size=1,
input_size=action_dim+cnn_output_dim,
**variant['qf_kwargs']
)
target_qf2 = MlpQfWithObsProcessor(
obs_processor=CNN(**cnn_params),
output_size=1,
input_size=action_dim+cnn_output_dim,
**variant['qf_kwargs']
)
action_dim = int(np.prod(env.action_space.shape))
policy_cnn = CNN(**cnn_params)
policy = TanhGaussianPolicyAdapter(
policy_cnn,
policy_cnn.conv_output_flat_size,
action_dim,
)
eval_env = expl_env = env
eval_policy = MakeDeterministic(policy)
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
**variant['eval_path_collector_kwargs']
)
replay_buffer = EnvReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
trainer = SACTrainer(
env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['trainer_kwargs']
)
if variant['collection_mode'] == 'batch':
expl_path_collector = MdpPathCollector(
expl_env,
policy,
**variant['expl_path_collector_kwargs']
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs']
)
elif variant['collection_mode'] == 'online':
expl_path_collector = MdpStepCollector(
expl_env,
policy,
**variant['expl_path_collector_kwargs']
)
algorithm = TorchOnlineRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs']
)
elif variant['collection_mode'] == 'parallel':
expl_path_collector = MdpPathCollector(
expl_env,
policy,
**variant['expl_path_collector_kwargs']
)
algorithm = TorchParallelRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs']
)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
from multiworld.envs.mujoco import register_goal_example_envs
register_goal_example_envs()
eval_env = gym.make('Image48SawyerPushForwardEnv-v0')
expl_env = gym.make('Image48SawyerPushForwardEnv-v0')
# Hack for now
eval_env.wrapped_env.transpose = True
expl_env.wrapped_env.transpose = True
# More hacks, use a dense reward instead
eval_env.wrapped_env.wrapped_env.reward_type = 'puck_distance'
expl_env.wrapped_env.wrapped_env.reward_type = 'puck_distance'
img_width, img_height = eval_env.image_shape
num_channels = 3
action_dim = int(np.prod(eval_env.action_space.shape))
cnn_params = variant['cnn_params']
cnn_params.update(
input_width=img_width,
input_height=img_height,
input_channels=num_channels,
added_fc_input_size=4,
output_conv_channels=True,
output_size=None,
)
qf_cnn = CNN(**cnn_params)
qf_obs_processor = nn.Sequential(
qf_cnn,
Flatten(),
)
qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
qf_kwargs['obs_processor'] = qf_obs_processor
qf_kwargs['output_size'] = 1
qf_kwargs['input_size'] = (action_dim + qf_cnn.conv_output_flat_size)
qf1 = MlpQfWithObsProcessor(**qf_kwargs)
qf2 = MlpQfWithObsProcessor(**qf_kwargs)
target_qf_cnn = CNN(**cnn_params)
target_qf_obs_processor = nn.Sequential(
target_qf_cnn,
Flatten(),
)
target_qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
target_qf_kwargs['obs_processor'] = target_qf_obs_processor
target_qf_kwargs['output_size'] = 1
target_qf_kwargs['input_size'] = (action_dim +
target_qf_cnn.conv_output_flat_size)
target_qf1 = MlpQfWithObsProcessor(**target_qf_kwargs)
target_qf2 = MlpQfWithObsProcessor(**target_qf_kwargs)
action_dim = int(np.prod(eval_env.action_space.shape))
policy_cnn = CNN(**cnn_params)
policy_obs_processor = nn.Sequential(
policy_cnn,
Flatten(),
)
policy = TanhGaussianPolicyAdapter(policy_obs_processor,
policy_cnn.conv_output_flat_size,
action_dim, **variant['policy_kwargs'])
eval_policy = MakeDeterministic(policy)
eval_path_collector = MdpPathCollector(
eval_env, eval_policy, **variant['eval_path_collector_kwargs'])
replay_buffer = EnvReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
trainer = SACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['trainer_kwargs'])
if variant['collection_mode'] == 'batch':
expl_path_collector = MdpPathCollector(
expl_env, policy, **variant['expl_path_collector_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
elif variant['collection_mode'] == 'online':
expl_path_collector = MdpStepCollector(
expl_env, policy, **variant['expl_path_collector_kwargs'])
algorithm = TorchOnlineRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
import multiworld
multiworld.register_all_envs()
env = gym.make('Image48SawyerReachXYEnv-v1')
observation_key = 'image_proprio_observation'
input_width, input_height = env.image_shape
action_dim = int(np.prod(env.action_space.shape))
cnn_params = variant['cnn_params']
cnn_params.update(
input_width=input_width,
input_height=input_height,
input_channels=3,
added_fc_input_size=3,
output_conv_channels=True,
output_size=None,
)
if variant['shared_qf_conv']:
qf_cnn = CNN(**cnn_params)
qf1 = MlpQfWithObsProcessor(
obs_processor=nn.Sequential(qf_cnn, Flatten()),
output_size=1,
input_size=action_dim + qf_cnn.conv_output_flat_size,
**variant['qf_kwargs'])
qf2 = MlpQfWithObsProcessor(
obs_processor=nn.Sequential(qf_cnn, Flatten()),
output_size=1,
input_size=action_dim + qf_cnn.conv_output_flat_size,
**variant['qf_kwargs'])
target_qf_cnn = CNN(**cnn_params)
target_qf1 = MlpQfWithObsProcessor(
obs_processor=nn.Sequential(target_qf_cnn, Flatten()),
output_size=1,
input_size=action_dim + target_qf_cnn.conv_output_flat_size,
**variant['qf_kwargs'])
target_qf2 = MlpQfWithObsProcessor(
obs_processor=nn.Sequential(target_qf_cnn, Flatten()),
output_size=1,
input_size=action_dim + target_qf_cnn.conv_output_flat_size,
**variant['qf_kwargs'])
else:
qf1_cnn = CNN(**cnn_params)
cnn_output_dim = qf1_cnn.conv_output_flat_size
qf1 = MlpQfWithObsProcessor(obs_processor=nn.Sequential(
qf1_cnn, Flatten()),
output_size=1,
input_size=action_dim + cnn_output_dim,
**variant['qf_kwargs'])
qf2 = MlpQfWithObsProcessor(obs_processor=nn.Sequential(
CNN(**cnn_params), Flatten()),
output_size=1,
input_size=action_dim + cnn_output_dim,
**variant['qf_kwargs'])
target_qf1 = MlpQfWithObsProcessor(
obs_processor=nn.Sequential(CNN(**cnn_params), Flatten()),
output_size=1,
input_size=action_dim + cnn_output_dim,
**variant['qf_kwargs'])
target_qf2 = MlpQfWithObsProcessor(
obs_processor=nn.Sequential(CNN(**cnn_params), Flatten()),
output_size=1,
input_size=action_dim + cnn_output_dim,
**variant['qf_kwargs'])
policy_cnn = CNN(**cnn_params)
policy = TanhGaussianPolicyAdapter(nn.Sequential(policy_cnn, Flatten()),
policy_cnn.conv_output_flat_size,
action_dim, **variant['policy_kwargs'])
eval_env = expl_env = env
eval_policy = MakeDeterministic(policy)
eval_path_collector = ObsDictPathCollector(
eval_env,
eval_policy,
observation_key=observation_key,
**variant['eval_path_collector_kwargs'])
replay_buffer = ObsDictReplayBuffer(
variant['replay_buffer_size'],
expl_env,
observation_key=observation_key,
**variant['replay_buffer_kwargs'],
)
trainer = SACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['trainer_kwargs'])
if variant['collection_mode'] == 'batch':
expl_path_collector = ObsDictPathCollector(
expl_env,
policy,
observation_key=observation_key,
**variant['expl_path_collector_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
elif variant['collection_mode'] == 'online':
expl_path_collector = ObsDictStepCollector(
expl_env,
policy,
observation_key=observation_key,
**variant['expl_path_collector_kwargs'])
algorithm = TorchOnlineRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
expl_env = roboverse.make(variant['env'],
gui=False,
randomize=True,
observation_mode=variant['obs'],
reward_type='shaped',
transpose_image=True)
eval_env = expl_env
img_width, img_height = eval_env.image_shape
num_channels = 3
action_dim = int(np.prod(eval_env.action_space.shape))
# obs_dim = 11
cnn_params = variant['cnn_params']
cnn_params.update(
input_width=img_width,
input_height=img_height,
input_channels=num_channels,
added_fc_input_size=0,
output_conv_channels=True,
output_size=None,
)
qf_cnn = CNN(**cnn_params)
qf_obs_processor = nn.Sequential(
qf_cnn,
Flatten(),
)
qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
qf_kwargs['obs_processor'] = qf_obs_processor
qf_kwargs['output_size'] = 1
qf_kwargs['input_size'] = (action_dim + qf_cnn.conv_output_flat_size)
qf1 = MlpQfWithObsProcessor(**qf_kwargs)
qf2 = MlpQfWithObsProcessor(**qf_kwargs)
target_qf_cnn = CNN(**cnn_params)
target_qf_obs_processor = nn.Sequential(
target_qf_cnn,
Flatten(),
)
target_qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
target_qf_kwargs['obs_processor'] = target_qf_obs_processor
target_qf_kwargs['output_size'] = 1
target_qf_kwargs['input_size'] = (action_dim +
target_qf_cnn.conv_output_flat_size)
target_qf1 = MlpQfWithObsProcessor(**target_qf_kwargs)
target_qf2 = MlpQfWithObsProcessor(**target_qf_kwargs)
action_dim = int(np.prod(eval_env.action_space.shape))
policy_cnn = CNN(**cnn_params)
policy_obs_processor = nn.Sequential(
policy_cnn,
Flatten(),
)
policy = TanhGaussianPolicyAdapter(policy_obs_processor,
policy_cnn.conv_output_flat_size,
action_dim, **variant['policy_kwargs'])
eval_policy = MakeDeterministic(policy)
observation_key = 'image'
eval_path_collector = ObsDictPathCollector(
eval_env,
eval_policy,
observation_key=observation_key,
**variant['eval_path_collector_kwargs'])
expl_path_collector = CustomObsDictPathCollector(
expl_env,
observation_key=observation_key,
)
with open(variant['buffer'], 'rb') as f:
replay_buffer = pickle.load(f)
trainer = SACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
behavior_policy=None,
**variant['trainer_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
eval_both=True,
batch_rl=variant['load_buffer'],
**variant['algorithm_kwargs'])
video_func = VideoSaveFunctionBullet(variant)
algorithm.post_train_funcs.append(video_func)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
env_params = dict(
block_random=0.3,
camera_random=0,
simple_observations=False,
continuous=True,
remove_height_hack=True,
render_mode="DIRECT",
# render_mode="GUI",
num_objects=5,
max_num_training_models=900,
target=False,
test=False,
)
expl_env = FlatEnv(KukaGraspingProceduralEnv(**env_params))
eval_env = expl_env
img_width, img_height = eval_env.image_shape
num_channels = 3
action_dim = int(np.prod(eval_env.action_space.shape))
cnn_params = variant['cnn_params']
cnn_params.update(
input_width=img_width,
input_height=img_height,
input_channels=num_channels,
added_fc_input_size=0,
output_conv_channels=True,
output_size=None,
)
qf_cnn = CNN(**cnn_params)
qf_obs_processor = nn.Sequential(
qf_cnn,
Flatten(),
)
qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
qf_kwargs['obs_processor'] = qf_obs_processor
qf_kwargs['output_size'] = 1
qf_kwargs['input_size'] = (action_dim + qf_cnn.conv_output_flat_size)
qf1 = MlpQfWithObsProcessor(**qf_kwargs)
qf2 = MlpQfWithObsProcessor(**qf_kwargs)
target_qf_cnn = CNN(**cnn_params)
target_qf_obs_processor = nn.Sequential(
target_qf_cnn,
Flatten(),
)
target_qf_kwargs = copy.deepcopy(variant['qf_kwargs'])
target_qf_kwargs['obs_processor'] = target_qf_obs_processor
target_qf_kwargs['output_size'] = 1
target_qf_kwargs['input_size'] = (action_dim +
target_qf_cnn.conv_output_flat_size)
target_qf1 = MlpQfWithObsProcessor(**target_qf_kwargs)
target_qf2 = MlpQfWithObsProcessor(**target_qf_kwargs)
action_dim = int(np.prod(eval_env.action_space.shape))
policy_cnn = CNN(**cnn_params)
policy_obs_processor = nn.Sequential(
policy_cnn,
Flatten(),
)
policy = TanhGaussianPolicyAdapter(policy_obs_processor,
policy_cnn.conv_output_flat_size,
action_dim, **variant['policy_kwargs'])
observation_key = 'image'
eval_policy = MakeDeterministic(policy)
eval_path_collector = ObsDictPathCollector(
eval_env,
eval_policy,
observation_key=observation_key,
**variant['eval_path_collector_kwargs'])
replay_buffer = ObsDictReplayBuffer(
variant['replay_buffer_size'],
expl_env,
observation_key=observation_key,
)
trainer = SACTrainer(env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['trainer_kwargs'])
if variant['collection_mode'] == 'batch':
expl_path_collector = ObsDictPathCollector(
expl_env,
policy,
observation_key=observation_key,
**variant['expl_path_collector_kwargs'])
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
elif variant['collection_mode'] == 'online':
expl_path_collector = ObsDictStepCollector(
expl_env,
policy,
observation_key=observation_key,
**variant['expl_path_collector_kwargs'])
algorithm = TorchOnlineRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algo_kwargs'])
else:
raise NotImplementedError
video_func = VideoSaveFunctionBullet(variant)
algorithm.post_train_funcs.append(video_func)
# dump_buffer_func = BufferSaveFunction(variant)
# algorithm.post_train_funcs.append(dump_buffer_func)
algorithm.to(ptu.device)
algorithm.train()
