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Reinforcement Learning for Gym CarRacing-v0 with PyTorch

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Car Racing with PyTorch

Solving the car racing problem in OpenAI Gym using Proximal Policy Optimization (PPO). This problem has a real physical engine in the back end. You can achieve real racing actions in the environment, like drifting.

Requirement

To run the code, you need

Method

Every action will be repeated for 8 frames. To get velocity information, state is defined as adjacent 4 frames in shape (4, 96, 96). Use a two heads FCN to represent the actor and critic respectively. The actor outputs α, β for each actin as the parameters of Beta distribution.

Training

Start a Visdom server with python -m visdom.server, it will serve http://localhost:8097/ by default.

To train the agent, runpython train.py --render --vis or python train.py --render without visdom. To test, run python test.py --render.

Performance

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Reinforcement Learning for Gym CarRacing-v0 with PyTorch

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