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chore: release v0.1.0

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(cherry picked from commit 82525f882f3924a332d9ce40bf64255d0d14f6a4)
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motphys-developers
2026-01-04 04:43:04 +00:00
parent 13cfbce9a7
commit 62011bb24f
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docs/source/en/user_guide/demo/dm_walker.md
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# 2D Walker Robot Training Example
# 2D Walker Robot
The 2D Walker Robot (Walker2D) is a classic robot control task from DeepMind Control Suite. The goal is to achieve standing, walking, and running by controlling the robot's joints.
@@ -46,33 +46,17 @@ uv run scripts/train.py --env dm-runner
### 1. Environment Preview
```bash
# View standing task
uv run scripts/view.py --env dm-stander
# View walking task
uv run scripts/view.py --env dm-walker
# View running task
uv run scripts/view.py --env dm-runner
```
### 2. Start Training
```bash
# Train standing task
uv run scripts/train.py --env dm-stander
# Train walking task (default)
uv run scripts/train.py --env dm-walker
# Train running task
uv run scripts/train.py --env dm-runner
# Customize number of environments
uv run scripts/train.py --env dm-walker --num-envs 512
# Enable rendering (visualize during training)
uv run scripts/train.py --env dm-walker --render
```
### 3. View Training Progress
@@ -84,39 +68,9 @@ uv run tensorboard --logdir runs/dm-walker
### 4. Test Training Results
```bash
# Automatically find best policy for testing (recommended)
uv run scripts/play.py --env dm-stander
uv run scripts/play.py --env dm-walker
# Manually specify policy file for testing
uv run scripts/play.py --env dm-walker --policy runs/dm-walker/nn/best_policy.pickle
```
> **Tip**: The system will automatically find the latest and best policy files in the `runs/dm-walker/` directory for testing. Supports dm-stander, dm-walker, dm-runner three task modes.
## Configuration Parameters
### Environment Configuration
```python
@dataclass
class WalkerEnvCfg(EnvCfg):
model_file: str = "walker.xml" # MJCF model file
max_episode_seconds: float = 25.0 # Maximum episode length
sim_dt: float = 0.0125 # Simulation time step
ctrl_dt: float = 0.025 # Control time step
move_speed: float = 1.0 # Target movement speed
stand_height: float = 1.2 # Target standing height
```
### Training Configuration
```python
@dataclass
class WalkerRLCfg(BaseRLCfg):
num_envs: int = 512 # Number of parallel environments
learning_rate: float = 3e-4 # Learning rate
batch_size: int = 512 # Batch size
max_epochs: int = 1000 # Maximum training epochs
uv run scripts/play.py --env dm-runner
```
## Reward Function Design