fix: critic called once (was 3x), adaptive KL schedule restored, upgrade 3m

scripts/dreamwaq_sim2sim_mujoco.py

@@ -235,8 +235,8 @@ def main():
 
             # ONNX inference
             outputs = session.run(None, {
-                'observations': obs.reshape(1, -1).astype(np.float32),
-                'obs_history': history.astype(np.float32),
+                'obs': obs.reshape(1, -1).astype(np.float32),
+                'obs_history': history.reshape(1, -1).astype(np.float32),
             })
             action = outputs[0][0]
             action = np.clip(action, -CLIP_ACTIONS, CLIP_ACTIONS)

scripts/export_dreamwaq_onnx_new.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python3
+"""Export DreamWaQ CENetActorModel to ONNX for MuJoCo sim2sim.
+
+输入: obs(45) + obs_history(225) → 输出: action(12)（确定性，mean CENet code）
+
+用法:
+    uv run scripts/export_dreamwaq_onnx_new.py
+    uv run scripts/export_dreamwaq_onnx_new.py --checkpoint runs/.../model_2850.pt
+"""
+import argparse, os, sys, glob
+import torch
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from motrix_rl.rslrl.torch.models.cenet_actor import CENetActorModel
+from tensordict import TensorDict
+
+PROJECT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+class DwaqInfer(torch.nn.Module):
+    """确定性推理：VAE mean code + Actor MLP。"""
+    def __init__(self, model: CENetActorModel):
+        super().__init__()
+        self.vae = model.vae
+        self.actor_mlp = model.mlp
+
+    def forward(self, obs, obs_history):
+        """obs: (N,45), obs_history: (N,225) → action: (N,12)"""
+        code = self.vae.deterministic_code(obs_history)  # (N,19)
+        latent = torch.cat([code, obs], dim=-1)  # (N,64)
+        return self.actor_mlp(latent)
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--checkpoint", default=None)
+    p.add_argument("--output", default=None)
+    args = p.parse_args()
+
+    if args.checkpoint:
+        ckpt = args.checkpoint
+    else:
+        models = sorted(glob.glob(os.path.join(PROJECT, "runs/go1-dreamwaq-walk/rslrl/*/model_*.pt")))
+        if not models:
+            print("No checkpoints found"); sys.exit(1)
+        ckpt = models[-1]
+    print(f"[Export] checkpoint: {ckpt}")
+
+    # 加载模型
+    dummy = TensorDict({
+        "policy": torch.zeros(1, 45),
+        "obs_history": torch.zeros(1, 225),
+        "privileged_obs": torch.zeros(1, 247),
+    }, batch_size=[1])
+    model = CENetActorModel(
+        dummy, {"actor": ["policy", "obs_history"]}, "actor", 12,
+        hidden_dims=[512, 256, 128], activation="elu", stochastic=True,
+        init_noise_std=1.0, cenet_in_dim=225, cenet_out_dim=19,
+    )
+    data = torch.load(ckpt, map_location="cpu")
+    if "actor_state_dict" in data:
+        model.load_state_dict(data["actor_state_dict"])
+    else:
+        model.load_state_dict(data)
+    model.eval()
+
+    # 包装为推理模型
+    infer = DwaqInfer(model)
+    infer.eval()
+
+    # 导出 ONNX
+    out_path = args.output or os.path.join(os.path.dirname(ckpt), "policy.onnx")
+    dummy_obs = torch.zeros(1, 45)
+    dummy_hist = torch.zeros(1, 225)
+    torch.onnx.export(
+        infer, (dummy_obs, dummy_hist), out_path,
+        input_names=["obs", "obs_history"],
+        output_names=["actions"],
+        opset_version=11,
+        dynamic_axes={"obs": {0: "batch"}, "obs_history": {0: "batch"}, "actions": {0: "batch"}},
+    )
+    print(f"[Export] saved: {out_path}")
+
+
+if __name__ == "__main__":
+    main()