cyy/Motrixlab
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix: dual-shuffle bug in terrain_origins — robots now spawn on correct terrain columns

Browse Source
This commit is contained in:
8x54zj-m
2026-06-30 22:16:29 +08:00
parent cb62f35ef6
commit 1eede03ef0
2 changed files with 13 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
motrix_envs/src/motrix_envs/locomotion/go1/dreamwaq.py
View File

@@ -410,11 +410,16 @@ class DreamWaQTask(Go1WalkTask):
# 游戏启发式地形课程（per-env，与上游一致） # 游戏启发式地形课程（per-env，与上游一致）
if not hasattr(self, '_terrain_origins'): if not hasattr(self, '_terrain_origins'):
all_levels = np.repeat(np.arange(self._num_rows), self._num_cols) all_origins = np.zeros((self._num_rows, self._num_cols, 2), dtype=np.float32)
all_indices = np.tile(np.arange(self._num_cols), self._num_rows) half_x = self._border + self._num_cols * self._cell_size / 2.0
all_origins = self._make_origins(all_levels, all_indices) half_y = self._border + self._num_rows * self._cell_size / 2.0
self._terrain_origins = all_origins.reshape(self._num_rows, self._num_cols, 2) for row in range(self._num_rows):
self._max_init_level = 0 # 从平地起步，靠课程升级 for col in range(self._num_cols):
cx = -half_x + self._border + col * self._cell_size + self._cell_size / 2
cy = half_y - self._border - row * self._cell_size - self._cell_size / 2
all_origins[row, col] = [cx, cy]
self._terrain_origins = all_origins
self._max_init_level = 5 # 上游原值，随机 0-5 起步
if num_reset > 0 and self._init_done and state is not None and hasattr(state, 'info'): if num_reset > 0 and self._init_done and state is not None and hasattr(state, 'info'):
old_info = state.info old_info = state.info

6
motrix_rl/src/motrix_rl/tasks/go1.py
View File

@@ -112,7 +112,7 @@ class rslrl:
# Runner 设置（严格对齐上游 LeggedRobotCfgPPO + Go1RoughCfgPPO） # Runner 设置（严格对齐上游 LeggedRobotCfgPPO + Go1RoughCfgPPO）
runner.seed = 5 # 上游 seed=5 runner.seed = 5 # 上游 seed=5
runner.max_iterations = 3000 runner.max_iterations = 5000 # 2048 envs 需要更多迭代补偿采样量
runner.num_steps_per_env = 24 runner.num_steps_per_env = 24
runner.experiment_name = "go1_dreamwaq_walk" runner.experiment_name = "go1_dreamwaq_walk"
runner.save_interval = 50 runner.save_interval = 50
@@ -126,7 +126,7 @@ class rslrl:
runner.algorithm.entropy_coef = 0.01 # 上游 Go1RoughCfgPPO runner.algorithm.entropy_coef = 0.01 # 上游 Go1RoughCfgPPO
runner.algorithm.desired_kl = 0.01 # 上游 0.01 (默认 0.008) runner.algorithm.desired_kl = 0.01 # 上游 0.01 (默认 0.008)
runner.algorithm.clip_param = 0.2 runner.algorithm.clip_param = 0.2
runner.algorithm.schedule = "fixed" # 固定 schedule，防止 noise_std 发散 runner.algorithm.schedule = "adaptive" # 上游原值，前期 bug 已修
runner.algorithm.gamma = 0.99 runner.algorithm.gamma = 0.99
runner.algorithm.lam = 0.95 runner.algorithm.lam = 0.95
runner.algorithm.max_grad_norm = 1.0 runner.algorithm.max_grad_norm = 1.0
@@ -136,7 +136,7 @@ class rslrl:
runner.actor.class_name = ( runner.actor.class_name = (
"motrix_rl.rslrl.torch.models.cenet_actor:CENetActorModel") "motrix_rl.rslrl.torch.models.cenet_actor:CENetActorModel")
runner.actor.hidden_dims = [512, 256, 128] runner.actor.hidden_dims = [512, 256, 128]
runner.actor.init_noise_std = 0.5 # 降低探索噪声，1024 envs 不需要太高 runner.actor.init_noise_std = 1.0 # 上游原值，adaptive 会自行调节
# Critic：标准 MLPModel，输入 privileged_obs # Critic：标准 MLPModel，输入 privileged_obs
runner.critic.class_name = "MLPModel" runner.critic.class_name = "MLPModel"