added tdmpc2 to policy factory; shape fixes in tdmpc2

lerobot/common/policies/factory.py

@@ -51,6 +51,13 @@ def get_policy_and_config_classes(name: str) -> tuple[Policy, object]:
         from lerobot.common.policies.tdmpc.modeling_tdmpc import TDMPCPolicy
 
         return TDMPCPolicy, TDMPCConfig
+
+    elif name == "tdmpc2":
+        from lerobot.common.policies.tdmpc2.configuration_tdmpc2 import TDMPC2Config
+        from lerobot.common.policies.tdmpc2.modeling_tdmpc2 import TDMPC2Policy
+
+        return TDMPC2Policy, TDMPC2Config
+
     elif name == "diffusion":
         from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
         from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy

lerobot/common/policies/tdmpc2/modeling_tdmpc2.py

@@ -389,7 +389,7 @@ class TDMPC2Policy(
         reward_loss = (
             (
                 temporal_loss_coeffs
-                * soft_cross_entropy(reward_preds, reward, self.config)
+                * soft_cross_entropy(reward_preds, reward, self.config).mean(1)
                 * ~batch["next.reward_is_pad"]
                 * ~batch["observation.state_is_pad"][0]
                 * ~batch["action_is_pad"]
@@ -397,10 +397,11 @@ class TDMPC2Policy(
             .sum(0)
             .mean()
         )
+
         # Compute state-action value loss (TD loss) for all of the Q functions in the ensemble.
         ce_value_loss = 0.0
         for i in range(self.config.q_ensemble_size):
-            ce_value_loss += soft_cross_entropy(q_preds_ensemble[i], td_targets, self.config)
+            ce_value_loss += soft_cross_entropy(q_preds_ensemble[i], td_targets, self.config).mean(1)
 
         q_value_loss = (
             (
@@ -420,7 +421,6 @@ class TDMPC2Policy(
         # Calculate the advantage weighted regression loss for π as detailed in FOWM 3.1.
         # We won't need these gradients again so detach.
         z_preds = z_preds.detach()
-        self.model.change_q_grad(mode=False)
         action_preds, _, log_pis, _ = self.model.pi(z_preds[:-1])
 
         with torch.no_grad():
@@ -430,14 +430,9 @@ class TDMPC2Policy(
             self.scale.update(qs[0])
             qs = self.scale(qs)
 
-        rho = torch.pow(self.config.temporal_decay_coeff, torch.arange(len(qs), device=qs.device)).unsqueeze(
-            -1
-        )
-
         pi_loss = (
-            (self.config.entropy_coef * log_pis - qs).mean(dim=(1, 2))
-            * rho
-            # * temporal_loss_coeffs
+            (self.config.entropy_coef * log_pis - qs).mean(dim=2)
+            * temporal_loss_coeffs
             # `action_preds` depends on the first observation and the actions.
             * ~batch["observation.state_is_pad"][0]
             * ~batch["action_is_pad"]
@@ -447,7 +442,7 @@ class TDMPC2Policy(
             self.config.consistency_coeff * consistency_loss
             + self.config.reward_coeff * reward_loss
             + self.config.value_coeff * q_value_loss
-            + self.config.pi_coeff * pi_loss
+            + pi_loss
         )
 
         info.update(

lerobot/common/policies/tdmpc2/tdmpc2_utils.py

@@ -75,9 +75,6 @@ def soft_cross_entropy(pred, target, cfg):
     """Computes the cross entropy loss between predictions and soft targets."""
     pred = F.log_softmax(pred, dim=-1)
     target = two_hot(target, cfg)
-    import pudb
-
-    pudb.set_trace()
     return -(target * pred).sum(-1, keepdim=True)
 
 
@@ -137,16 +134,20 @@ def symexp(x):
 
 def two_hot(x, cfg):
     """Converts a batch of scalars to soft two-hot encoded targets for discrete regression."""
+
+    # x shape [horizon, num_features]
     if cfg.num_bins == 0:
         return x
     elif cfg.num_bins == 1:
         return symlog(x)
     x = torch.clamp(symlog(x), cfg.vmin, cfg.vmax)
-    bin_idx = torch.floor((x - cfg.vmin) / cfg.bin_size).long()
-    bin_offset = (x - cfg.vmin) / cfg.bin_size - bin_idx.float()
-    soft_two_hot = torch.zeros(x.size(0), cfg.num_bins, device=x.device)
-    soft_two_hot.scatter_(1, bin_idx, 1 - bin_offset)
-    soft_two_hot.scatter_(1, (bin_idx + 1) % cfg.num_bins, bin_offset)
+    bin_idx = torch.floor((x - cfg.vmin) / cfg.bin_size).long()  # shape [num_features]
+    bin_offset = ((x - cfg.vmin) / cfg.bin_size - bin_idx.float()).unsqueeze(-1)  # shape [num_features , 1]
+    soft_two_hot = torch.zeros(
+        *x.shape, cfg.num_bins, device=x.device
+    )  # shape [horizon, num_features, num_bins]
+    soft_two_hot.scatter_(2, bin_idx.unsqueeze(-1), 1 - bin_offset)
+    soft_two_hot.scatter_(2, (bin_idx.unsqueeze(-1) + 1) % cfg.num_bins, bin_offset)
     return soft_two_hot
 
 

lerobot/scripts/train.py

@@ -93,6 +93,18 @@ def make_optimizer_and_scheduler(cfg, policy):
     elif policy.name == "tdmpc":
         optimizer = torch.optim.Adam(policy.parameters(), cfg.training.lr)
         lr_scheduler = None
+
+    elif policy.name == "tdmpc2":
+        params_group = [
+            {"params": policy.model._encoder.parameters(), "lr": cfg.training.lr * cfg.training.enc_lr_scale},
+            {"params": policy.model._dynamics.parameters()},
+            {"params": policy.model._reward.parameters()},
+            {"params": policy.model._Qs.parameters()},
+            {"params": policy.model._pi.parameters(), "eps": 1e-5},
+        ]
+        optimizer = torch.optim.Adam(params_group, lr=cfg.training.lr)
+        lr_scheduler = None
+
     elif cfg.policy.name == "vqbet":
         from lerobot.common.policies.vqbet.modeling_vqbet import VQBeTOptimizer, VQBeTScheduler