diff --git a/lerobot/common/policies/sac/configuration_sac.py b/lerobot/common/policies/sac/configuration_sac.py index a324294c..5f676933 100644 --- a/lerobot/common/policies/sac/configuration_sac.py +++ b/lerobot/common/policies/sac/configuration_sac.py @@ -48,7 +48,7 @@ class SACConfig: critic_target_update_weight = 0.005 utd_ratio = 2 state_encoder_hidden_dim = 256 - latent_dim = 50 + latent_dim = 128 target_entropy = None critic_network_kwargs = { "hidden_dims": [256, 256], diff --git a/lerobot/common/policies/sac/modeling_sac.py b/lerobot/common/policies/sac/modeling_sac.py index 9df2c859..bd77408e 100644 --- a/lerobot/common/policies/sac/modeling_sac.py +++ b/lerobot/common/policies/sac/modeling_sac.py @@ -63,25 +63,35 @@ class SACPolicy( self.unnormalize_outputs = Unnormalize( config.output_shapes, config.output_normalization_modes, dataset_stats ) - encoder = SACObservationEncoder(config) + encoder_critic = SACObservationEncoder(config) + encoder_actor = SACObservationEncoder(config) # Define networks critic_nets = [] for _ in range(config.num_critics): - critic_net = Critic(encoder=encoder, network=MLP(**config.critic_network_kwargs)) + critic_net = Critic( + encoder=encoder_critic, + network=MLP( + input_dim=encoder_critic.output_dim + config.output_shapes["action"][0], + **config.critic_network_kwargs + ) + ) critic_nets.append(critic_net) self.critic_ensemble = create_critic_ensemble(critic_nets, config.num_critics) self.critic_target = deepcopy(self.critic_ensemble) self.actor = Policy( - encoder=encoder, - network=MLP(**config.actor_network_kwargs), + encoder=encoder_actor, + network=MLP( + input_dim=encoder_actor.output_dim, + **config.actor_network_kwargs + ), action_dim=config.output_shapes["action"][0], - **config.policy_kwargs, + **config.policy_kwargs ) if config.target_entropy is None: - config.target_entropy = -np.prod(config.output_shapes["action"][0]) # (-dim(A)) - self.temperature = LagrangeMultiplier(init_value=config.temperature_init) + config.target_entropy = -np.prod(config.output_shapes["action"][0]) # (-dim(A)) + self.temperature = LagrangeMultiplier(init_value=config.temperature_init) def reset(self): """ @@ -104,15 +114,31 @@ class SACPolicy( actions, _ = self.actor(batch) actions = self.unnormalize_outputs({"action": actions})["action"] return actions + + def critic_forward(self, observations: dict[str, Tensor], actions: Tensor, use_target: bool = False) -> Tensor: + """Forward pass through a critic network ensemble + + Args: + observations: Dictionary of observations + actions: Action tensor + use_target: If True, use target critics, otherwise use ensemble critics + + Returns: + Tensor of Q-values from all critics + """ + critics = self.critic_target if use_target else self.critic_ensemble + q_values = torch.stack([critic(observations, actions) for critic in critics]) + return q_values + def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor | float]: """Run the batch through the model and compute the loss. - + Returns a dictionary with loss as a tensor, and other information as native floats. """ batch = self.normalize_inputs(batch) - # batch shape is (b, 2, ...) where index 1 returns the current observation and - # the next observation for caluculating the right td index. + # batch shape is (b, 2, ...) where index 1 returns the current observation and + # the next observation for calculating the right td index. actions = batch["action"][:, 0] rewards = batch["next.reward"][:, 0] observations = {} @@ -121,113 +147,109 @@ class SACPolicy( if k.startswith("observation."): observations[k] = batch[k][:, 0] next_observations[k] = batch[k][:, 1] - + # perform image augmentation - # reward bias from HIL-SERL code base + # reward bias from HIL-SERL code base # add_or_replace={"rewards": batch["rewards"] + self.config["reward_bias"]} in reward_batch - + # calculate critics loss # 1- compute actions from policy action_preds, log_probs = self.actor(next_observations) # 2- compute q targets - q_targets = self.target_qs(next_observations, action_preds) + q_targets = self.critic_forward(next_observations, action_preds, use_target=True) + # subsample critics to prevent overfitting if use high UTD (update to date) if self.config.num_subsample_critics is not None: indices = torch.randperm(self.config.num_critics) - indices = indices[: self.config.num_subsample_critics] + indices = indices[:self.config.num_subsample_critics] q_targets = q_targets[indices] # critics subsample size - min_q = q_targets.min(dim=0) + min_q, _ = q_targets.min(dim=0) # Get values from min operation # compute td target - td_target = ( - rewards + self.config.discount * min_q - ) # + self.config.discount * self.temperature() * log_probs # add entropy term + td_target = rewards + self.config.discount * min_q #+ self.config.discount * self.temperature() * log_probs # add entropy term # 3- compute predicted qs - q_preds = self.critic_ensemble(observations, actions) + q_preds = self.critic_forward(observations, actions, use_target=False) # 4- Calculate loss # Compute state-action value loss (TD loss) for all of the Q functions in the ensemble. - # critics_loss = ( - # ( - # F.mse_loss( - # q_preds, - # einops.repeat(td_target, "t b -> e t b", e=q_preds.shape[0]), - # reduction="none", - # ).sum(0) # sum over ensemble - # # `q_preds_ensemble` depends on the first observation and the actions. - # * ~batch["observation.state_is_pad"][0] - # * ~batch["action_is_pad"] - # # q_targets depends on the reward and the next observations. - # * ~batch["next.reward_is_pad"] - # * ~batch["observation.state_is_pad"][1:] - # ) - # .sum(0) - # .mean() - # ) - # 4- Calculate loss - # Compute state-action value loss (TD loss) for all of the Q functions in the ensemble. - critics_loss = ( - F.mse_loss( - q_preds, # shape: [num_critics, batch_size] - einops.repeat( - td_target, "b -> e b", e=q_preds.shape[0] - ), # expand td_target to match q_preds shape - reduction="none", - ) - .sum(0) - .mean() - ) + critics_loss = F.mse_loss( + q_preds, # shape: [num_critics, batch_size] + einops.repeat(td_target, "b -> e b", e=q_preds.shape[0]), # expand td_target to match q_preds shape + reduction="none" + ).sum(0).mean() + # critics_loss = ( + # F.mse_loss( + # q_preds, + # einops.repeat(td_target, "b -> e b", e=q_preds.shape[0]), + # reduction="none", + # ).sum(0) # sum over ensemble + # # `q_preds_ensemble` depends on the first observation and the actions. + # * ~batch["observation.state_is_pad"][0] + # * ~batch["action_is_pad"] + # # q_targets depends on the reward and the next observations. + # * ~batch["next.reward_is_pad"] + # * ~batch["observation.state_is_pad"][1:] + # ).sum(0).mean() + # calculate actors loss # 1- temperature temperature = self.temperature() # 2- get actions (batch_size, action_dim) and log probs (batch_size,) actions, log_probs = self.actor(observations) # 3- get q-value predictions - with torch.no_grad(): - q_preds = self.critic_ensemble(observations, actions, return_type="mean") + with torch.inference_mode(): + q_preds = self.critic_forward(observations, actions, use_target=False) actor_loss = ( -(q_preds - temperature * log_probs).mean() - * ~batch["observation.state_is_pad"][0] - * ~batch["action_is_pad"] + # * ~batch["observation.state_is_pad"][0] + # * ~batch["action_is_pad"] ).mean() + # calculate temperature loss # 1- calculate entropy entropy = -log_probs.mean() - temperature_loss = self.temp(lhs=entropy, rhs=self.config.target_entropy) + temperature_loss = self.temperature( + lhs=entropy, + rhs=self.config.target_entropy + ) loss = critics_loss + actor_loss + temperature_loss return { - "critics_loss": critics_loss.item(), - "actor_loss": actor_loss.item(), - "temperature_loss": temperature_loss.item(), - "temperature": temperature.item(), - "entropy": entropy.item(), - "loss": loss, - } - + "critics_loss": critics_loss.item(), + "actor_loss": actor_loss.item(), + "temperature_loss": temperature_loss.item(), + "temperature": temperature.item(), + "entropy": entropy.item(), + "loss": loss, + } + def update(self): - self.critic_target.lerp_(self.critic_ensemble, self.config.critic_target_update_weight) # TODO: implement UTD update # First update only critics for utd_ratio-1 times - # for critic_step in range(self.config.utd_ratio - 1): - # only update critic and critic target + #for critic_step in range(self.config.utd_ratio - 1): + # only update critic and critic target # Then update critic, critic target, actor and temperature - - # for target_param, param in zip(self.critic_target.parameters(), self.critic_ensemble.parameters()): - # target_param.data.copy_(target_param.data * (1.0 - self.config.critic_target_update_weight) + param.data * self.critic_target_update_weight) - - + """Update target networks with exponential moving average""" + with torch.no_grad(): + for target_critic, critic in zip(self.critic_target, self.critic_ensemble, strict=False): + for target_param, param in zip(target_critic.parameters(), critic.parameters(), strict=False): + target_param.data.copy_( + target_param.data * self.config.critic_target_update_weight + + param.data * (1.0 - self.config.critic_target_update_weight) + ) + class MLP(nn.Module): def __init__( self, + input_dim: int, hidden_dims: list[int], activations: Callable[[torch.Tensor], torch.Tensor] | str = nn.SiLU(), activate_final: bool = False, @@ -236,46 +258,52 @@ class MLP(nn.Module): super().__init__() self.activate_final = activate_final layers = [] - - for i, size in enumerate(hidden_dims): - layers.append(nn.Linear(hidden_dims[i - 1] if i > 0 else hidden_dims[0], size)) - + + # First layer uses input_dim + layers.append(nn.Linear(input_dim, hidden_dims[0])) + + # Add activation after first layer + if dropout_rate is not None and dropout_rate > 0: + layers.append(nn.Dropout(p=dropout_rate)) + layers.append(nn.LayerNorm(hidden_dims[0])) + layers.append(activations if isinstance(activations, nn.Module) else getattr(nn, activations)()) + + # Rest of the layers + for i in range(1, len(hidden_dims)): + layers.append(nn.Linear(hidden_dims[i-1], hidden_dims[i])) + if i + 1 < len(hidden_dims) or activate_final: if dropout_rate is not None and dropout_rate > 0: layers.append(nn.Dropout(p=dropout_rate)) - layers.append(nn.LayerNorm(size)) - layers.append( - activations if isinstance(activations, nn.Module) else getattr(nn, activations)() - ) - + layers.append(nn.LayerNorm(hidden_dims[i])) + layers.append(activations if isinstance(activations, nn.Module) else getattr(nn, activations)()) + self.net = nn.Sequential(*layers) - def forward(self, x: torch.Tensor, train: bool = False) -> torch.Tensor: - # in training mode or not. TODO: find better way to do this - self.train(train) + def forward(self, x: torch.Tensor) -> torch.Tensor: return self.net(x) - - + + class Critic(nn.Module): def __init__( self, encoder: Optional[nn.Module], network: nn.Module, init_final: Optional[float] = None, - device: str = "cuda", + device: str = "cuda" ): super().__init__() self.device = torch.device(device) self.encoder = encoder self.network = network self.init_final = init_final - + # Find the last Linear layer's output dimension for layer in reversed(network.net): if isinstance(layer, nn.Linear): out_features = layer.out_features break - + # Output layer if init_final is not None: self.output_layer = nn.Linear(out_features, 1) @@ -284,17 +312,22 @@ class Critic(nn.Module): else: self.output_layer = nn.Linear(out_features, 1) orthogonal_init()(self.output_layer.weight) - + self.to(self.device) - def forward(self, observations: torch.Tensor, actions: torch.Tensor, train: bool = False) -> torch.Tensor: - self.train(train) - - observations = observations.to(self.device) + def forward( + self, + observations: dict[str, torch.Tensor], + actions: torch.Tensor, + ) -> torch.Tensor: + # Move each tensor in observations to device + observations = { + k: v.to(self.device) for k, v in observations.items() + } actions = actions.to(self.device) - + obs_enc = observations if self.encoder is None else self.encoder(observations) - + inputs = torch.cat([obs_enc, actions], dim=-1) x = self.network(inputs) value = self.output_layer(x) @@ -312,7 +345,7 @@ class Policy(nn.Module): fixed_std: Optional[torch.Tensor] = None, init_final: Optional[float] = None, use_tanh_squash: bool = False, - device: str = "cuda", + device: str = "cuda" ): super().__init__() self.device = torch.device(device) @@ -323,13 +356,13 @@ class Policy(nn.Module): self.log_std_max = log_std_max self.fixed_std = fixed_std.to(self.device) if fixed_std is not None else None self.use_tanh_squash = use_tanh_squash - + # Find the last Linear layer's output dimension for layer in reversed(network.net): if isinstance(layer, nn.Linear): out_features = layer.out_features break - + # Mean layer self.mean_layer = nn.Linear(out_features, action_dim) if init_final is not None: @@ -337,7 +370,7 @@ class Policy(nn.Module): nn.init.uniform_(self.mean_layer.bias, -init_final, init_final) else: orthogonal_init()(self.mean_layer.weight) - + # Standard deviation layer or parameter if fixed_std is None: self.std_layer = nn.Linear(out_features, action_dim) @@ -346,20 +379,21 @@ class Policy(nn.Module): nn.init.uniform_(self.std_layer.bias, -init_final, init_final) else: orthogonal_init()(self.std_layer.weight) - + self.to(self.device) def forward( - self, + self, observations: torch.Tensor, ) -> Tuple[torch.Tensor, torch.Tensor]: + # Encode observations if encoder exists - obs_enc = observations if self.encoder is not None else self.encoder(observations) + obs_enc = observations if self.encoder is None else self.encoder(observations) # Get network outputs outputs = self.network(obs_enc) means = self.mean_layer(outputs) - + # Compute standard deviations if self.fixed_std is None: log_std = self.std_layer(outputs) @@ -367,25 +401,25 @@ class Policy(nn.Module): log_std = torch.tanh(log_std) log_std = torch.clamp(log_std, self.log_std_min, self.log_std_max) else: - stds = self.fixed_std.expand_as(means) - + log_std = self.fixed_std.expand_as(means) + # uses tahn activation function to squash the action to be in the range of [-1, 1] - normal = torch.distributions.Normal(means, stds) - x_t = normal.rsample() # for reparameterization trick (mean + std * N(0,1)) + normal = torch.distributions.Normal(means, torch.exp(log_std)) + x_t = normal.rsample() # for reparameterization trick (mean + std * N(0,1)) log_probs = normal.log_prob(x_t) if self.use_tanh_squash: actions = torch.tanh(x_t) log_probs -= torch.log((1 - actions.pow(2)) + 1e-6) - log_probs = log_probs.sum(-1) # sum over action dim + log_probs = log_probs.sum(-1) # sum over action dim return actions, log_probs - + def get_features(self, observations: torch.Tensor) -> torch.Tensor: """Get encoded features from observations""" observations = observations.to(self.device) if self.encoder is not None: - with torch.no_grad(): - return self.encoder(observations, train=False) + with torch.inference_mode(): + return self.encoder(observations) return observations @@ -459,43 +493,56 @@ class SACObservationEncoder(nn.Module): feat.append(self.env_state_enc_layers(obs_dict["observation.environment_state"])) if "observation.state" in self.config.input_shapes: feat.append(self.state_enc_layers(obs_dict["observation.state"])) + # TODO(ke-wang): currently average over all features, concatenate all features maybe a better way return torch.stack(feat, dim=0).mean(0) + + @property + def output_dim(self) -> int: + """Returns the dimension of the encoder output""" + return self.config.latent_dim class LagrangeMultiplier(nn.Module): - def __init__(self, init_value: float = 1.0, constraint_shape: Sequence[int] = (), device: str = "cuda"): + def __init__( + self, + init_value: float = 1.0, + constraint_shape: Sequence[int] = (), + device: str = "cuda" + ): super().__init__() self.device = torch.device(device) init_value = torch.log(torch.exp(torch.tensor(init_value, device=self.device)) - 1) - + # Initialize the Lagrange multiplier as a parameter self.lagrange = nn.Parameter( torch.full(constraint_shape, init_value, dtype=torch.float32, device=self.device) ) - + self.to(self.device) - def forward(self, lhs: Optional[torch.Tensor] = None, rhs: Optional[torch.Tensor] = None) -> torch.Tensor: - # Get the multiplier value based on parameterization + def forward( + self, + lhs: Optional[torch.Tensor | float | int] = None, + rhs: Optional[torch.Tensor | float | int] = None + ) -> torch.Tensor: + # Get the multiplier value based on parameterization multiplier = torch.nn.functional.softplus(self.lagrange) - + # Return the raw multiplier if no constraint values provided if lhs is None: return multiplier - - # Move inputs to device - lhs = lhs.to(self.device) + + # Convert inputs to tensors and move to device + lhs = torch.tensor(lhs, device=self.device) if not isinstance(lhs, torch.Tensor) else lhs.to(self.device) if rhs is not None: - rhs = rhs.to(self.device) - - # Use the multiplier to compute the Lagrange penalty - if rhs is None: + rhs = torch.tensor(rhs, device=self.device) if not isinstance(rhs, torch.Tensor) else rhs.to(self.device) + else: rhs = torch.zeros_like(lhs, device=self.device) - + diff = lhs - rhs - + assert diff.shape == multiplier.shape, f"Shape mismatch: {diff.shape} vs {multiplier.shape}" - + return multiplier * diff @@ -508,7 +555,6 @@ def create_critic_ensemble(critics: list[nn.Module], num_critics: int, device: s assert len(critics) == num_critics, f"Expected {num_critics} critics, got {len(critics)}" return nn.ModuleList(critics).to(device) - # borrowed from tdmpc def flatten_forward_unflatten(fn: Callable[[Tensor], Tensor], image_tensor: Tensor) -> Tensor: """Helper to temporarily flatten extra dims at the start of the image tensor. @@ -516,7 +562,7 @@ def flatten_forward_unflatten(fn: Callable[[Tensor], Tensor], image_tensor: Tens Args: fn: Callable that the image tensor will be passed to. It should accept (B, C, H, W) and return (B, *), where * is any number of dimensions. - image_tensor: An image tensor of shape (**, C, H, W), where ** is any number of dimensions and + image_tensor: An image tensor of shape (**, C, H, W), where ** is any number of dimensions and can be more than 1 dimensions, generally different from *. Returns: A return value from the callable reshaped to (**, *). @@ -526,4 +572,4 @@ def flatten_forward_unflatten(fn: Callable[[Tensor], Tensor], image_tensor: Tens start_dims = image_tensor.shape[:-3] inp = torch.flatten(image_tensor, end_dim=-4) flat_out = fn(inp) - return torch.reshape(flat_out, (*start_dims, *flat_out.shape[1:])) + return torch.reshape(flat_out, (*start_dims, *flat_out.shape[1:])) \ No newline at end of file