This is a PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".
This implementation is inspired by Universe Starter Agent. As in the starter agent, I don't share parameters of the optimizers between threads. If you want to have the same optimizer as in the original paper by DeepMind, you might want to check this implementation.
Contributions are very welcome. If you know how to make this code better, don't hesitate to send a pull request.
python main.py --env-name "PongDeterministic-v3" --num-processes 16
This code runs evaluation in a separate thread in addition to 16 processes.
With 16 processes it converges for PongDeterministic-v3 in 15 minutes.
For BreakoutDeterministic-v3 it takes more than several hours.