racing-rl

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reinforcement learning for f1tenth racing

racing-rl 최신버젼 다운로드

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# Racing-RL

reinforcement learning for f1tenth racing

# How to start

The implementation has been tested with `Python 3.9` under `Ubuntu 20.04`.

Installation:

1. Clone this repo.
2. Then initialize submodules `f1tenth_gym` and `f1tenth_racetracks`:
    ```
    git submodule init
    git submodule update
    ```
3. Install `f1tenth_gym`:
   ```
   cd f1tenth_gym
   pip install --user -e gym
   ```
4. Install requirements:
   ```
   pip install -r requirements.txt
   ```
5. Run a sample training:
   ```
   python train.py --track melbourne --algo ppo --reward min_action -include_velocity
   ```
   This will run a default training configuration (`20K` steps, approx. 7 min) and save the logs.
6. Offline evaluation of a trained model on a different track:
   ```
   python test.py --track nuerburgring --checkpoint <path_to_logdir>/models/best_model.zip --n_episodes 1 
   ```

# Current status

- Observation space
    - 2d-map from lidar scan + velocity
    - 2d-map from lidar scan + max frame-aggregation (like atari)
    - 2d-map from lidar scan + stack frame-aggregation (on channel dim)
- Action space (def. in `racing_rl/envs/single_agent_env.py:action_space`)
    - only steering: steering `+/-0.41 rad`, fixed speed = `2 m/s`
    - both controls: steering `+/-0.41 rad`, speed `[0,10] m/s`
- Reward definitions
    - progress (`racing_rl/rewards/progress_based.py`):
        - the reward is proportional to its progress w.r.t. the centerline, optionally using a penalty for collision
    - min_action (`racing_rl/rewards/min_action.py`):
        - the reward is inversely proportional to the actions' deviation from the mid-value of the action domain (
          steering=0.0 rad, speed=5.0 m/s)

# Known bugs

- [ ] add render-mode `rgb_array` to store video during the training process
- [ ] `track.get_progress` does not correctly manage the crossing of the starting-line (from `0.99` to `1.99`)

# Open questions
- [ ] find stable problem configuration w.r.t. the following questions:
    - [ ] what is the minimal observation space? (ideally only lidar-based)
    - [ ] what is the less-restrictive action space? (ideally constrained only by action ranges)
    - [ ] what is a simple reward that enable good training?

# TODO
- [ ] refactor the code structure, e.g., `make_base_env` is getting messy with a lot of wrappers
- [ ] tune base algorithms (e.g., Optuna)

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