Feature/dv3 parallel stochastic

sheeprl/algos/dreamer_v3/agent.py

@@ -13,6 +13,7 @@
 from torch import Tensor, device, nn
 from torch.distributions import Distribution, Independent, Normal, TanhTransform, TransformedDistribution
 from torch.distributions.utils import probs_to_logits
+from torch.nn.modules import Module
 
 from sheeprl.algos.dreamer_v2.agent import WorldModel
 from sheeprl.algos.dreamer_v2.utils import compute_stochastic_state
@@ -461,6 +462,93 @@ def imagination(self, prior: Tensor, recurrent_state: Tensor, actions: Tensor) -
         return imagined_prior, recurrent_state
 
 
+class DecoupledRSSM(RSSM):
+    """RSSM model for the model-base Dreamer agent.
+
+    Args:
+        recurrent_model (nn.Module): the recurrent model of the RSSM model described in
+            [https://arxiv.org/abs/1811.04551](https://arxiv.org/abs/1811.04551).
+        representation_model (nn.Module): the representation model composed by a
+            multi-layer perceptron to compute the stochastic part of the latent state.
+            For more information see [https://arxiv.org/abs/2010.02193](https://arxiv.org/abs/2010.02193).
+        transition_model (nn.Module): the transition model described in
+            [https://arxiv.org/abs/2010.02193](https://arxiv.org/abs/2010.02193).
+            The model is composed by a multi-layer perceptron to predict the stochastic part of the latent state.
+        distribution_cfg (Dict[str, Any]): the configs of the distributions.
+        discrete (int, optional): the size of the Categorical variables.
+            Defaults to 32.
+        unimix: (float, optional): the percentage of uniform distribution to inject into the categorical
+            distribution over states, i.e. given some logits `l` and probabilities `p = softmax(l)`,
+            then `p = (1 - self.unimix) * p + self.unimix * unif`, where `unif = `1 / self.discrete`.
+            Defaults to 0.01.
+    """
+
+    def __init__(
+        self,
+        recurrent_model: Module,
+        representation_model: Module,
+        transition_model: Module,
+        distribution_cfg: Dict[str, Any],
+        discrete: int = 32,
+        unimix: float = 0.01,
+    ) -> None:
+        super().__init__(recurrent_model, representation_model, transition_model, distribution_cfg, discrete, unimix)
+
+    def dynamic(
+        self, posterior: Tensor, recurrent_state: Tensor, action: Tensor, is_first: Tensor
+    ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
+        """
+        Perform one step of the dynamic learning:
+            Recurrent model: compute the recurrent state from the previous latent space, the action taken by the agent,
+                i.e., it computes the deterministic state (or ht).
+            Transition model: predict the prior from the recurrent output.
+            Representation model: compute the posterior from the recurrent state and from
+                the embedded observations provided by the environment.
+        For more information see [https://arxiv.org/abs/1811.04551](https://arxiv.org/abs/1811.04551)
+        and [https://arxiv.org/abs/2010.02193](https://arxiv.org/abs/2010.02193).
+
+        Args:
+            posterior (Tensor): the stochastic state computed by the representation model (posterior). It is expected
+                to be of dimension `[stoch_size, self.discrete]`, which by default is `[32, 32]`.
+            recurrent_state (Tensor): a tuple representing the recurrent state of the recurrent model.
+            action (Tensor): the action taken by the agent.
+            embedded_obs (Tensor): the embedded observations provided by the environment.
+            is_first (Tensor): if this is the first step in the episode.
+
+        Returns:
+            The recurrent state (Tensor): the recurrent state of the recurrent model.
+            The posterior stochastic state (Tensor): computed by the representation model
+            The prior stochastic state (Tensor): computed by the transition model
+            The logits of the posterior state (Tensor): computed by the transition model from the recurrent state.
+            The logits of the prior state (Tensor): computed by the transition model from the recurrent state.
+            from the recurrent state and the embbedded observation.
+        """
+        action = (1 - is_first) * action
+        recurrent_state = (1 - is_first) * recurrent_state + is_first * torch.tanh(torch.zeros_like(recurrent_state))
+        posterior = posterior.view(*posterior.shape[:-2], -1)
+        posterior = (1 - is_first) * posterior + is_first * self._transition(recurrent_state, sample_state=False)[
+            1
+        ].view_as(posterior)
+        recurrent_state = self.recurrent_model(torch.cat((posterior, action), -1), recurrent_state)
+        prior_logits, prior = self._transition(recurrent_state)
+        return recurrent_state, prior, prior_logits
+
+    def _representation(self, embedded_obs: Tensor) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+            embedded_obs (Tensor): the embedded real observations provided by the environment.
+
+        Returns:
+            logits (Tensor): the logits of the distribution of the posterior state.
+            posterior (Tensor): the sampled posterior stochastic state.
+        """
+        logits: Tensor = self.representation_model(embedded_obs)
+        logits = self._uniform_mix(logits)
+        return logits, compute_stochastic_state(
+            logits, discrete=self.discrete, validate_args=self.distribution_cfg.validate_args
+        )
+
+
 class PlayerDV3(nn.Module):
     """
     The model of the Dreamer_v3 player.
@@ -486,7 +574,7 @@ class PlayerDV3(nn.Module):
     def __init__(
         self,
         encoder: _FabricModule,
-        rssm: RSSM,
+        rssm: RSSM | DecoupledRSSM,
         actor: _FabricModule,
         actions_dim: Sequence[int],
         num_envs: int,
@@ -495,10 +583,15 @@ def __init__(
         device: device = "cpu",
         discrete_size: int = 32,
         actor_type: str | None = None,
+        decoupled_rssm: bool = False,
     ) -> None:
         super().__init__()
         self.encoder = encoder
-        self.rssm = RSSM(
+        if decoupled_rssm:
+            rssm_cls = DecoupledRSSM
+        else:
+            rssm_cls = RSSM
+        self.rssm = rssm_cls(
             recurrent_model=rssm.recurrent_model.module,
             representation_model=rssm.representation_model.module,
             transition_model=rssm.transition_model.module,
@@ -515,6 +608,7 @@ def __init__(
         self.num_envs = num_envs
         self.validate_args = self.actor.distribution_cfg.validate_args
         self.actor_type = actor_type
+        self.decoupled_rssm = decoupled_rssm
 
     @torch.no_grad()
     def init_states(self, reset_envs: Optional[Sequence[int]] = None) -> None:
@@ -580,7 +674,10 @@ def get_greedy_action(
         self.recurrent_state = self.rssm.recurrent_model(
             torch.cat((self.stochastic_state, self.actions), -1), self.recurrent_state
         )
-        _, self.stochastic_state = self.rssm._representation(self.recurrent_state, embedded_obs)
+        if self.decoupled_rssm:
+            _, self.stochastic_state = self.rssm._representation(embedded_obs)
+        else:
+            _, self.stochastic_state = self.rssm._representation(self.recurrent_state, embedded_obs)
         self.stochastic_state = self.stochastic_state.view(
             *self.stochastic_state.shape[:-2], self.stochastic_size * self.discrete_size
         )
@@ -976,8 +1073,11 @@ def build_agent(
         **world_model_cfg.recurrent_model,
         input_size=int(sum(actions_dim) + stochastic_size),
     )
+    represention_model_input_size = encoder.output_dim
+    if not cfg.algo.decoupled_rssm:
+        represention_model_input_size += recurrent_state_size
     representation_model = MLP(
-        input_dims=recurrent_state_size + encoder.cnn_output_dim + encoder.mlp_output_dim,
+        input_dims=represention_model_input_size,
         output_dim=stochastic_size,
         hidden_sizes=[world_model_cfg.representation_model.hidden_size],
         activation=eval(world_model_cfg.representation_model.dense_act),
@@ -1004,7 +1104,11 @@ def build_agent(
             else None
         ),
     )
-    rssm = RSSM(
+    if cfg.algo.decoupled_rssm:
+        rssm_cls = DecoupledRSSM
+    else:
+        rssm_cls = RSSM
+    rssm = rssm_cls(
         recurrent_model=recurrent_model.apply(init_weights),
         representation_model=representation_model.apply(init_weights),
         transition_model=transition_model.apply(init_weights),

sheeprl/algos/dreamer_v3/dreamer_v3.py

@@ -108,23 +108,43 @@ def train(
     # Dynamic Learning
     stoch_state_size = stochastic_size * discrete_size
     recurrent_state = torch.zeros(1, batch_size, recurrent_state_size, device=device)
-    posterior = torch.zeros(1, batch_size, stochastic_size, discrete_size, device=device)
     recurrent_states = torch.empty(sequence_length, batch_size, recurrent_state_size, device=device)
     priors_logits = torch.empty(sequence_length, batch_size, stoch_state_size, device=device)
-    posteriors = torch.empty(sequence_length, batch_size, stochastic_size, discrete_size, device=device)
-    posteriors_logits = torch.empty(sequence_length, batch_size, stoch_state_size, device=device)
 
     # Embed observations from the environment
     embedded_obs = world_model.encoder(batch_obs)
 
-    for i in range(0, sequence_length):
-        recurrent_state, posterior, _, posterior_logits, prior_logits = world_model.rssm.dynamic(
-            posterior, recurrent_state, batch_actions[i : i + 1], embedded_obs[i : i + 1], data["is_first"][i : i + 1]
-        )
-        recurrent_states[i] = recurrent_state
-        priors_logits[i] = prior_logits
-        posteriors[i] = posterior
-        posteriors_logits[i] = posterior_logits
+    if cfg.algo.decoupled_rssm:
+        posteriors_logits, posteriors = world_model.rssm._representation(embedded_obs)
+        for i in range(0, sequence_length):
+            if i == 0:
+                posterior = torch.zeros_like(posteriors[:1])
+            else:
+                posterior = posteriors[i - 1 : i]
+            recurrent_state, posterior_logits, prior_logits = world_model.rssm.dynamic(
+                posterior,
+                recurrent_state,
+                batch_actions[i : i + 1],
+                data["is_first"][i : i + 1],
+            )
+            recurrent_states[i] = recurrent_state
+            priors_logits[i] = prior_logits
+    else:
+        posterior = torch.zeros(1, batch_size, stochastic_size, discrete_size, device=device)
+        posteriors = torch.empty(sequence_length, batch_size, stochastic_size, discrete_size, device=device)
+        posteriors_logits = torch.empty(sequence_length, batch_size, stoch_state_size, device=device)
+        for i in range(0, sequence_length):
+            recurrent_state, posterior, _, posterior_logits, prior_logits = world_model.rssm.dynamic(
+                posterior,
+                recurrent_state,
+                batch_actions[i : i + 1],
+                embedded_obs[i : i + 1],
+                data["is_first"][i : i + 1],
+            )
+            recurrent_states[i] = recurrent_state
+            priors_logits[i] = prior_logits
+            posteriors[i] = posterior
+            posteriors_logits[i] = posterior_logits
     latent_states = torch.cat((posteriors.view(*posteriors.shape[:-2], -1), recurrent_states), -1)
 
     # Compute predictions for the observations
@@ -457,6 +477,7 @@ def main(fabric: Fabric, cfg: Dict[str, Any]):
         cfg.algo.world_model.recurrent_model.recurrent_state_size,
         fabric.device,
         discrete_size=cfg.algo.world_model.discrete_size,
+        decoupled_rssm=cfg.algo.decoupled_rssm,
     )
 
     # Optimizers

sheeprl/algos/dreamer_v3/evaluate.py

@@ -63,6 +63,7 @@ def evaluate(fabric: Fabric, cfg: Dict[str, Any], state: Dict[str, Any]):
         cfg.algo.world_model.recurrent_model.recurrent_state_size,
         fabric.device,
         discrete_size=cfg.algo.world_model.discrete_size,
+        decoupled_rssm=cfg.algo.decoupled_rssm,
     )
 
     test(player, fabric, cfg, log_dir, sample_actions=True)

sheeprl/configs/algo/dreamer_v3.yaml

@@ -33,6 +33,7 @@ dense_act: torch.nn.SiLU
 cnn_act: torch.nn.SiLU
 unimix: 0.01
 hafner_initialization: True
+decoupled_rssm: False
 
 # World model
 world_model: