dqn_pytorch

Introduction

This implementation follows the benchmark and implementations of DQN. The training steps are bounded by 50M with epsilon is 0.1. Testing epsilon is 0.05.

Usage

python dqn.py

You are able to change the env in dqn.py

Benchmark Results

Testing score is calculated by averaging 15 testing episodes every 200,000 training steps, and is compared with the best scores reported in the paper - DQN. After the paper score is reached, the game is terminated.

Requirements

• python 3.6
• gym
• cv2
• pytorch-1.1.0
• numpy
• 35+GB memory