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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
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docs/source/en/user_guide/demo/cartpole.md
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# CartPole Training Example
# CartPole
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)
@@ -21,14 +21,7 @@ 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
@@ -40,63 +33,11 @@ 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