54 lines
1.4 KiB
Markdown
54 lines
1.4 KiB
Markdown
# CartPole
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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.
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## Task Description
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- **State Space**: Cart position, cart velocity, pole angle, pole angular velocity
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- **Action Space**: Apply force left or right
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- **Reward Function**: +1 reward for each step the pole stays upright
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- **Termination Conditions**: Pole angle exceeds ±15 degrees or episode length exceeds 10 seconds
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## Quick Start
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### 1. Environment Preview
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```bash
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uv run scripts/view.py --env cartpole
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```
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### 2. Start Training
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```bash
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uv run scripts/train.py --env cartpole
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```
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### 3. View Training Progress
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```bash
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uv run tensorboard --logdir runs/cartpole
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```
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### 4. Test Training Results
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```bash
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uv run scripts/play.py --env cartpole
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```
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> **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.
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## Expected Results
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- Pole angle stays within ±5 degrees most of the time
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- Cart displacement range is reasonable
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## Troubleshooting
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If training performance is poor, you can try:
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1. Adjust learning rate (try 1e-4 to 1e-3)
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2. Increase number of environments (more parallel training)
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3. Adjust reward function weights
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4. Check if physical parameters are reasonable
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