This repository includes an implementation of Q-Learning and policy gradient to solve this Open AI gym environment https://gym.openai.com/envs/CarRacing-v0/ using PyTorch.
More details in the source file: https://github.com/openai/gym/blob/master/gym/envs/box2d/car_racing.py
Tested with:
- Ubuntu 18.04
- Nvidia RTX 2070 card
- Cuda 10.2
- CuDNN 7.6.5
pip install -r requirements.txt
./play.sh
TODO:
- Describe action spaces
- Add more insights (e.g. increasing the experience buffer works)
TODO
TODO
Models, configurations and outputs on Google Drive.
| Model name | Main configuration improvement(s) |
|---|---|
| model_basic_openai_stop_expl | Stop after 50 negative consecutive rewards |
| Model name | Training episodes | Test average score (10 runs *) | Short example | Notes |
|---|---|---|---|---|
| model_basic_openai_stop_expl | 450 | 690 |  |
It fails the tight curves, but not every time. It is able to rejoin the track from the grass in some situations. |
| model_basic_openai_stop_expl | 500 | 723 |  |
Almost perfect guide, the limit on the score is the prudence on gas. It fails in rare tight curve situations. |
| model_basic_openai_stop_expl | 550 | N/A | N/A | More on gas, but completely wrong with only 50 more training episodes. |
(*) Test runs should be around 100 to be reliable.
TODO