chore: release v0.0.1

docs/source/en/user_guide/demo/dm_walker.md

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+# 2D Walker Robot Training Example
+
+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.
+
+```{video} /_static/videos/dm_walker.mp4
+:poster: _static/images/poster/dm_walker.jpg
+:nocontrols:
+:autoplay:
+:playsinline:
+:muted:
+:loop:
+:width: 100%
+```
+
+## Task Description
+
+Walker2D is a 2D planar bipedal robot with multiple joints and actuators:
+
+-   **State Space**: Includes rotation angles and angular velocities of various robot parts, torso height and velocity, etc.
+-   **Action Space**: Control torques for each joint
+-   **Reward Function**: Mainly composed of maintaining standing balance and forward speed
+-   **Termination Conditions**: Robot falls or joints reach limit positions
+
+### Three Task Modes
+
+1. **dm-stander**: Static standing task (move_speed = 0.0)
+
+```bash
+uv run scripts/train.py --env dm-stander
+```
+
+2. **dm-walker**: Walking task (move_speed = 1.0)
+
+```bash
+uv run scripts/train.py --env dm-walker
+```
+
+3. **dm-runner**: Running task (move_speed = 5.0)
+
+```bash
+uv run scripts/train.py --env dm-runner
+```
+
+## Quick Start
+
+### 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
+
+```bash
+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-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
+```
+
+## Reward Function Design
+
+Walker2D's reward function consists of the following components:
+
+### Basic Standing Reward
+
+```python
+# Height reward: keep torso at target height
+
+# Upright reward: keep torso upright
+```
+
+### Movement Reward (walking and running tasks)
+
+```python
+# Speed reward: track target speed
+
+# Total reward = standing reward * movement weight
+```
+
+## Expected Results
+
+1. **dm-stander**:
+
+    - Torso height maintained in 1.0-1.4m range
+
+2. **dm-walker**:
+
+    - Actual walking speed close to 1.0 m/s
+
+3. **dm-runner**:
+    - Running speed reaches 4.0-5.0 m/s