fix: storage dones/rewards shape squeeze, NaN guard in AdaBoot

motrix_rl/src/motrix_rl/rslrl/torch/train/dreamwaq_ppo.py

@@ -181,8 +181,8 @@ class DreamWaQPPO(PPO):
         """
         if not hasattr(self, '_adaboot_reward_buf'):
             self._adaboot_reward_buf = []
-        dones = storage.dones                   # (T, E)  bool
-        rewards = storage.rewards               # (T, E)  每步奖励
+        dones = storage.dones.squeeze(-1)       # (T, E, 1) → (T, E)
+        rewards = storage.rewards.squeeze(-1)   # (T, E, 1) → (T, E)
         if dones is None or rewards is None:
             return
         T, E = dones.shape
@@ -197,7 +197,8 @@ class DreamWaQPPO(PPO):
                 if dones[t, e].item():
                     # episode 结束：累计从 start 到 t 的奖励
                     ep_sum = rewards[start:t + 1, e].sum().item()
-                    ep_rewards.append(ep_sum)
+                    if abs(ep_sum) < 1e9:  # 过滤 NaN/Inf
+                        ep_rewards.append(ep_sum)
                     start = t + 1
 
         if len(ep_rewards) < 8:
@@ -213,12 +214,14 @@ class DreamWaQPPO(PPO):
         if len(self._adaboot_reward_buf) < 50:
             return
 
-        # 论文公式: CV = σ / μ
+        # 论文公式: CV = σ / μ（NaN 防护）
         buf = self._adaboot_reward_buf
         mean_r = sum(buf) / len(buf)
         var_r = sum((r - mean_r) ** 2 for r in buf) / len(buf)
         std_r = var_r ** 0.5
         cv = std_r / (mean_r + 1e-6)
+        if not (0 <= cv < 1e6):  # NaN 或 Inf → 保持当前 prob
+            return
         # CV → bootstrap 概率（CV 高→不稳定→多用 GT；映射系数 5.0 可调）
         self.actor._adaboot_prob = max(0.0, min(1.0, cv * 5.0))
 

motrix_rl/src/motrix_rl/tasks/go1.py

@@ -105,14 +105,14 @@ class rslrl:
     class Go1DreamWaQWalkRslrlPpo(RslrlCfg):
         """Go1 DreamWaQ walk — CENet VAE + 不对称特权观测。"""
 
-        num_envs: int = 2048
+        num_envs: int = 4096  # 论文原值
 
         def __post_init__(self):
             runner = self.runner
 
             # Runner 设置（严格对齐上游 LeggedRobotCfgPPO + Go1RoughCfgPPO）
             runner.seed = 5                     # 上游 seed=5
-            runner.max_iterations = 5000  # 2048 envs 需要更多迭代补偿采样量
+            runner.max_iterations = 1000  # 论文原值
             runner.num_steps_per_env = 24
             runner.experiment_name = "go1_dreamwaq_walk"
             runner.save_interval = 50