chore: release v0.0.1

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

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+# CartPole Training Example
+
+CartPole is a classic control task in reinforcement learning. The goal is to keep the pole balanced by controlling the cart's left-right movement.
+![cartpole](/_static/images/poster/cartpole.jpg)
+
+## Task Description
+
+-   **State Space**: Cart position, cart velocity, pole angle, pole angular velocity
+-   **Action Space**: Apply force left or right
+-   **Reward Function**: +1 reward for each step the pole stays upright
+-   **Termination Conditions**: Pole angle exceeds ±15 degrees or episode length exceeds 10 seconds
+
+## Quick Start
+
+### 1. Environment Preview
+
+```bash
+uv run scripts/view.py --env cartpole
+```
+
+### 2. Start Training
+
+```bash
+# Train with default parameters
+uv run scripts/train.py --env cartpole
+
+# Customize number of environments
+uv run scripts/train.py --env cartpole --num-envs 1024
+
+# Enable rendering (visualize during training)
+uv run scripts/train.py --env cartpole --render
+```
+
+### 3. View Training Progress
+
+```bash
+uv run tensorboard --logdir runs/cartpole
+```
+
+### 4. Test Training Results
+
+```bash
+# Automatically find best policy for testing (recommended)
+uv run scripts/play.py --env cartpole
+
+# Manually specify policy file for testing
+uv run scripts/play.py --env cartpole --policy runs/cartpole/nn/best_policy.pickle
+```
+
+> **Tip**: The system will automatically find the latest and best policy files in the `runs/cartpole/` directory for testing. You can also manually specify specific policy files using the `--policy` parameter.
+
+## Configuration Parameters
+
+Main configuration parameters for the CartPole environment:
+
+```python
+@dataclass
+class CartPoleEnvCfg(EnvCfg):
+    model_file: str = "path/to/inverted_pendulum.xml"  # MJCF model file
+    reset_noise_scale: float = 0.01                    # Reset noise
+    max_episode_seconds: float = 10.0                 # Maximum episode length
+```
+
+Training configuration parameters:
+
+```python
+from dataclasses import dataclass
+from motrix_rl.skrl.cfg import PPOCfg
+from motrix_rl import registry
+
+@registry.rlcfg("cartpole")
+@dataclass
+class CartPolePPO(PPOCfg):
+    max_env_steps: int = 10_000_000          # Maximum environment steps
+    check_point_interval: int = 500          # Checkpoint interval
+
+    # Network structure (small network suitable for simple tasks)
+    policy_hidden_layer_sizes: tuple[int, ...] = (32, 32)
+    value_hidden_layer_sizes: tuple[int, ...] = (32, 32)
+
+    # PPO parameters
+    rollouts: int = 32                       # Experience replay rounds
+    learning_epochs: int = 5                 # Training rounds
+    mini_batches: int = 4                    # Number of mini-batches
+```
+
+**Note**: CartPole is a simple task and currently uses universal configuration. If you need to create specialized configurations for different training backends (JAX/Torch), refer to the environment configuration documentation examples.
+
+## Custom Training
+
+You can override default configurations through command line arguments:
+
+```bash
+uv run scripts/train.py --env cartpole \
+  --num-envs 1024 \
+  --train-backend jax \
+  --sim-backend np
+```
+
+## Expected Results
+
+-   Pole angle stays within ±5 degrees most of the time
+-   Cart displacement range is reasonable
+
+## Troubleshooting
+
+If training performance is poor, you can try:
+
+1. Adjust learning rate (try 1e-4 to 1e-3)
+2. Increase number of environments (more parallel training)
+3. Adjust reward function weights
+4. Check if physical parameters are reasonable