def test_rollouts():
action_size = 10
obs_embed_size = 100
stochastic_size = 30
deterministic_size = 200
batch_size = 4
time_steps = 10
transition_model = RSSMTransition(action_size, stochastic_size,
deterministic_size)
representation_model = RSSMRepresentation(transition_model, obs_embed_size,
action_size, stochastic_size,
deterministic_size)
rollout_module = RSSMRollout(representation_model, transition_model)
obs_embed: torch.Tensor = torch.randn(time_steps, batch_size,
obs_embed_size)
action: torch.Tensor = torch.randn(time_steps, batch_size, action_size)
prev_state: RSSMState = representation_model.initial_state(batch_size)
prior, post = rollout_module(time_steps, obs_embed, action, prev_state)
assert isinstance(prior, RSSMState)
assert isinstance(post, RSSMState)
assert prior.mean.shape == (time_steps, batch_size, stochastic_size)
assert post.mean.shape == (time_steps, batch_size, stochastic_size)
assert prior.std.shape == (time_steps, batch_size, stochastic_size)
assert post.std.shape == (time_steps, batch_size, stochastic_size)
assert prior.stoch.shape == (time_steps, batch_size, stochastic_size)
assert post.stoch.shape == (time_steps, batch_size, stochastic_size)
assert prior.deter.shape == (time_steps, batch_size, deterministic_size)
assert post.deter.shape == (time_steps, batch_size, deterministic_size)
prior = rollout_module.rollout_transition(
time_steps, action, transition_model.initial_state(batch_size))
assert isinstance(prior, RSSMState)
assert prior.mean.shape == (time_steps, batch_size, stochastic_size)
assert prior.std.shape == (time_steps, batch_size, stochastic_size)
assert prior.stoch.shape == (time_steps, batch_size, stochastic_size)
assert prior.deter.shape == (time_steps, batch_size, deterministic_size)
def policy(state):
action = torch.randn(state.stoch.size(0), action_size)
mean = torch.randn(state.stoch.size(0), action_size)
std = torch.randn(state.stoch.size(0), action_size)
action_dist = SampleDist(torch.distributions.Normal(mean, std))
return action, action_dist
prior, actions = rollout_module.rollout_policy(time_steps, policy,
post[-1])
assert isinstance(prior, RSSMState)
assert prior.mean.shape == (time_steps, batch_size, stochastic_size)
assert prior.std.shape == (time_steps, batch_size, stochastic_size)
assert prior.stoch.shape == (time_steps, batch_size, stochastic_size)
assert prior.deter.shape == (time_steps, batch_size, deterministic_size)
assert isinstance(actions, torch.Tensor)
assert actions.shape == (time_steps, batch_size, action_size)
def __init__(
self,
action_shape,
stochastic_size=30,
deterministic_size=200,
hidden_size=200,
image_shape=(3, 64, 64),
action_hidden_size=200,
action_layers=3,
action_dist='one_hot',
reward_shape=(1, ),
reward_layers=3,
reward_hidden=300,
value_shape=(1, ),
value_layers=3,
value_hidden=200,
dtype=torch.float,
use_pcont=False,
pcont_layers=3,
pcont_hidden=200,
**kwargs,
):
super().__init__()
self.observation_encoder = ObservationEncoder(shape=image_shape)
encoder_embed_size = self.observation_encoder.embed_size
decoder_embed_size = stochastic_size + deterministic_size
self.observation_decoder = ObservationDecoder(
embed_size=decoder_embed_size, shape=image_shape)
self.action_shape = action_shape
output_size = np.prod(action_shape)
self.transition = RSSMTransition(output_size, stochastic_size,
deterministic_size, hidden_size)
self.representation = RSSMRepresentation(self.transition,
encoder_embed_size,
output_size, stochastic_size,
deterministic_size,
hidden_size)
self.rollout = RSSMRollout(self.representation, self.transition)
feature_size = stochastic_size + deterministic_size
self.action_size = output_size
self.action_dist = action_dist
self.action_decoder = ActionDecoder(output_size, feature_size,
action_hidden_size, action_layers,
action_dist)
self.reward_model = DenseModel(feature_size, reward_shape,
reward_layers, reward_hidden)
self.value_model = DenseModel(feature_size, value_shape, value_layers,
value_hidden)
self.dtype = dtype
self.stochastic_size = stochastic_size
self.deterministic_size = deterministic_size
if use_pcont:
self.pcont = DenseModel(feature_size, (1, ),
pcont_layers,
pcont_hidden,
dist='binary')
def __init__(
self,
action_shape,
stochastic_size=30,
deterministic_size=200,
hidden_size=200,
image_shape=(3, 64, 64),
stride=2,
depth=32,
padding=0,
action_hidden_size=200,
action_layers=3,
action_dist='one_hot',
reward_shape=(1, ),
reward_layers=3,
reward_hidden=300,
value_shape=(1, ),
value_layers=3,
value_hidden=200,
dtype=torch.float,
state_size=None,
use_pcont=False,
pcont_layers=3,
pcont_hidden=200,
full_conv=True,
**kwargs,
):
super().__init__()
if full_conv:
EncoderClass = ObservationEncoder
DecoderClass = ObservationDecoder
else:
EncoderClass = MiniObservationEncoder
DecoderClass = MiniObservationDecoder
self.observation_encoder = EncoderClass(shape=image_shape,
stride=stride,
depth=depth,
padding=padding)
encoder_embed_size = self.observation_encoder.embed_size
if state_size is not None:
encoder_embed_size += state_size
decoder_embed_size = stochastic_size + deterministic_size
self.observation_decoder = DecoderClass(embed_size=decoder_embed_size,
shape=image_shape,
stride=stride)
self.action_shape = action_shape
output_size = np.prod(action_shape)
self.transition = RSSMTransition(output_size, stochastic_size,
deterministic_size, hidden_size)
self.representation = RSSMRepresentation(self.transition,
encoder_embed_size,
output_size, stochastic_size,
deterministic_size,
hidden_size)
self.rollout = RSSMRollout(self.representation, self.transition)
feature_size = stochastic_size + deterministic_size
self.action_size = output_size
self.action_dist = action_dist
self.action_decoder = ActionDecoder(output_size, feature_size,
action_hidden_size, action_layers,
action_dist)
self.reward_model = DenseModel(feature_size, reward_shape,
reward_layers, reward_hidden)
self.value_model = DenseModel(feature_size, value_shape, value_layers,
value_hidden)
self.dtype = dtype
self.stochastic_size = stochastic_size
self.deterministic_size = deterministic_size
self.use_state = state_size is not None
self.state_size = state_size
if use_pcont:
self.pcont = DenseModel(feature_size, (1, ),
pcont_layers,
pcont_hidden,
dist='binary')