prepare unembedding parameters in ActionEmbedder as well as the policy head, to allow for behavioral cloning before RL

dreamer4/dreamer4.py

@@ -282,7 +282,9 @@ class ActionEmbedder(Module):
         *,
         num_discrete_actions: int | tuple[int, ...] = 0,
         num_continuous_actions  = 0,
-        continuous_norm_stats: tuple[tuple[float, float], ...] | None = None
+        continuous_norm_stats: tuple[tuple[float, float], ...] | None = None,
+        can_unembed = False,
+        unembed_dim = None
     ):
         super().__init__()
 
@@ -294,6 +296,8 @@ class ActionEmbedder(Module):
         self.num_discrete_action_types = len(num_discrete_actions)
         self.discrete_action_embed = Embedding(total_discrete_actions, dim)
 
+        self.register_buffer('num_discrete_actions', num_discrete_actions, persistent = False)
+
         # continuous actions
 
         self.num_continuous_action_types = num_continuous_actions
@@ -314,6 +318,15 @@ class ActionEmbedder(Module):
         offsets = F.pad(num_discrete_actions.cumsum(dim = -1), (1, -1), value = 0)
         self.register_buffer('discrete_action_offsets', offsets, persistent = False)
 
+        # unembedding
+
+        self.can_unembed = can_unembed
+
+        if can_unembed:
+            unembed_dim = default(unembed_dim, dim)
+            self.discrete_action_unembed = Parameter(torch.randn(total_discrete_actions, unembed_dim) * 1e-2)
+            self.continuous_action_unembed = Parameter(torch.randn(num_continuous_actions, unembed_dim, 2) * 1e-2)
+
     @property
     def device(self):
         return self.discrete_action_offsets.device
@@ -322,6 +335,40 @@ class ActionEmbedder(Module):
     def has_actions(self):
         return self.num_discrete_action_types > 0 or self.num_continuous_action_types > 0
 
+    def unembed(
+        self,
+        embeds,                           # (... d)
+        discrete_action_types = None,    # (na)
+        continuous_action_types = None,  # (na)
+
+    ):  # (... discrete_na), (... continuous_na 2)
+
+        assert self.can_unembed, 'can only unembed for predicted discrete and continuous actions if `can_unembed = True` is set on init'
+
+        assert not exists(discrete_action_types), 'selecting subset of discrete action types to unembed not completed yet'
+
+        # discrete actions
+
+        discrete_action_logits = None
+
+        if self.num_discrete_action_types > 0:
+            discrete_action_logits = einsum(embeds, self.discrete_action_unembed, '... d, na d -> ... na')
+
+        # continuous actions
+
+        continuous_action_mean_log_var = None
+
+        if self.num_continuous_action_types > 0:
+
+            continuous_action_unembed = self.continuous_action_unembed
+
+            if exists(continuous_action_types):
+                continuous_action_unembed = continuous_action_unembed[continuous_action_types]
+
+            continuous_action_mean_log_var = einsum(embeds, continuous_action_unembed, '... d, na d two -> ... na two')
+
+        return discrete_action_logits, continuous_action_mean_log_var
+
     def forward(
         self,
         *,
@@ -1220,6 +1267,7 @@ class DynamicsWorldModel(Module):
         continuous_norm_stats = None,
         reward_loss_weight = 0.1,
         value_head_mlp_depth = 3,
+        policy_head_mlp_depth = 3,
         num_latent_genes = 0                        # for carrying out evolution within the dreams https://web3.arxiv.org/abs/2503.19037
     ):
         super().__init__()
@@ -1338,6 +1386,15 @@ class DynamicsWorldModel(Module):
 
         self.reward_loss_weight = reward_loss_weight
 
+        # policy head
+
+        self.policy_head = create_mlp(
+            dim_in = dim,
+            dim = dim * 4,
+            dim_out = dim,
+            depth = policy_head_mlp_depth
+        )
+
         # value head
 
         self.value_head = create_mlp(

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "dreamer4"
-version = "0.0.8"
+version = "0.0.9"
 description = "Dreamer 4"
 authors = [
     { name = "Phil Wang", email = "lucidrains@gmail.com" }

tests/test_dreamer.py

@@ -251,3 +251,22 @@ def test_action_embedder():
     action_embed = embedder(discrete_actions = discrete_actions, continuous_actions = continuous_actions, discrete_action_types = 1, continuous_action_types = 0)
 
     assert action_embed.shape == (2, 3, 512)
+
+    # unembed
+
+    embedder = ActionEmbedder(
+        512,
+        num_discrete_actions = (4, 4),
+        num_continuous_actions = 2,
+        can_unembed = True
+    )
+
+    discrete_actions = torch.randint(0, 4, (2, 3, 2))
+    continuous_actions = torch.randn(2, 3, 2)
+
+    action_embed = embedder(discrete_actions = discrete_actions, continuous_actions = continuous_actions)
+
+    discrete_logits, continuous_mean_log_var = embedder.unembed(action_embed)
+
+    assert discrete_logits.shape == (2, 3, 8)
+    assert continuous_mean_log_var.shape == (2, 3, 2, 2)