pytorch-a3c

xvfb-run -s "-screen 0 1400x900x24" python main.py

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. In contrast to the starter agent, it uses an optimizer with shared statistics as in the original paper.

Contibutions

Contributions are very welcome. If you know how to make this code better, don't hesitate to send a pull request.

Usage

OMP_NUM_THREADS=1 python main.py --env-name "PongDeterministic-v3" --num-processes 16

This code runs evaluation in a separate thread in addition to 16 processes.

Note: Install most recent nightly build (version '0.1.10+2fd4d08' or later) of PyTorch via this command to prevent memory leaks: pip install git+https://github.com/pytorch/pytorch

Results

With 16 processes it converges for PongDeterministic-v3 in 15 minutes.

For BreakoutDeterministic-v3 it takes more than several hours.