feat: DreamWaQ full replication — env, terrain, CENet, PPO

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# DreamWaQ 复刻进度
## 总览
| Phase | 状态 | 完成时间 |
|-------|------|----------|
| Phase 1: 环境差距补齐 | ✅ | 2026-06-30 |
| Phase 2: 地形生成 10×20 | ✅ | 2026-06-30 |
| Phase 3: CENet 网络集成 | ✅ | 2026-06-30 |
| Phase 4: 训练 Pipeline | ✅ | 2026-06-30 |
| Phase 5: 验证 | ⏳ | - |
## Phase 3+4: CENet + 训练
**新增文件**
- `motrix_rl/src/motrix_rl/rslrl/torch/models/cenet_actor.py` — CENetVAE + CENetActorModel
- `motrix_rl/src/motrix_rl/rslrl/torch/train/dreamwaq_ppo.py` — DreamWaQPPOPPO + VAE loss
**修改文件**
- `wrap_vec_env.py` — 多键 TensorDictpolicy + obs_history + privileged_obs
- `cfg.py` — 添加 vae_beta/cenet_in_dim/cenet_out_dim
- `go1.py` — go1-dreamwaq-walk 训练配置
**冒烟测试**1 iteration 通过VAE 损失 ≈ 2.85
### 数据流
```
DreamWaQTask.update_observation()
→ state.info["obs_history"] (N, 5, 45)
→ state.info["privileged_obs"] (N, 247)
RslrlNpEnvWrap._build_obs_dict()
→ TensorDict({
"policy": (N, 45),
"obs_history": (N, 225),
"privileged_obs": (N, 247),
})
CENetActorModel.get_latent()
→ VAE(obs_history) → code(19)
→ cat(code, policy) → latent(64)
→ MLP[512,256,128] → action(12)
DreamWaQPPO.update()
→ + VAE loss速度估计 + 重建 + KL
```
## 训练命令
```bash
# 平坦地形
uv run scripts/train.py --env go1-dreamwaq-walk --rllib rslrl
# 金字塔地形
DREAMWAQ_TERRAIN=pyramid uv run scripts/train.py --env go1-dreamwaq-walk --rllib rslrl
```

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docs/dreamwaq_usage.md Normal file
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# DreamWaQ 使用手册
## 环境切换
通过环境变量 `DREAMWAQ_TERRAIN` 选择地形场景:
| 值 | 场景 | 描述 |
|----|------|------|
| `flat` (默认) | 无限平面 | 学习基础行走 |
| `pyramid` | 10×20 混合地形 | hfield + mesh 楼梯 |
| `flat_stairs` | 2 级 flat+stairs | 简易楼梯测试 |
| `stairs` | 纯楼梯 | stair box 场景 |
```bash
# 金字塔地形训练
DREAMWAQ_TERRAIN=pyramid uv run scripts/train.py --env go1-dreamwaq-walk --rllib rslrl
# 平坦地形训练
uv run scripts/train.py --env go1-dreamwaq-walk --rllib rslrl
```
## 地形生成
```bash
# 完整 10×20 地形(生成 PNG + OBJ + XML
uv run python3 scripts/gen_dreamwaq_terrain.py
# 仅前 N 个难度级别(测试用)
uv run python3 scripts/gen_dreamwaq_terrain.py --max-level 3
# 仅平坦(不生成楼梯)
uv run python3 scripts/gen_dreamwaq_terrain.py --flat-only
```
## 可视化
```bash
# 查看金字塔地形(随机动作)
uv run scripts/view_dreamwaq.py
# 平坦地形 + 单机器人 + 固定难度
uv run scripts/view_dreamwaq.py --flat --num-envs 1 --level 5
# 持续前进(不站立)
uv run scripts/view_dreamwaq.py --no-stand --vx 0.8
```
## 训练
```bash
# 启动训练
uv run scripts/train.py --env go1-dreamwaq-walk --rllib rslrl
# 指定环境数量
uv run scripts/train.py --env go1-dreamwaq-walk --rllib rslrl --num-envs 4096
```
训练结果保存在 `runs/go1-dreamwaq-walk/rslrl/`TensorBoard 日志自动记录:
- `value_loss`, `surrogate_loss` — 标准 PPO 损失
- `autoenc_loss` — CENet VAE 自编码器损失
```bash
# 查看训练曲线
uv run tensorboard --logdir runs/go1-dreamwaq-walk
```
## Play评估
```bash
# 自动发现最新 checkpoint
uv run scripts/play_dreamwaq_rsl.py
# 指定 checkpoint + 命令
uv run scripts/play_dreamwaq_rsl.py --checkpoint runs/go1-dreamwaq-walk/rslrl/.../model_1000.pt --vx 0.5
# 固定地形级别查看
uv run scripts/play_dreamwaq_rsl.py --terrain --level 5 --num-envs 1
```