StrangerNing / dqn_pytorch

DQN with pytorch with on Breakout and SpaceInvaders

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.

Env	Best testing score	# of training steps when best score reached
Breakout	403.666667	19000000 (19M)
SpaceInvaders	1996.333333	11600000 (~12M)
Seaquest	7274.666667	8600000 (8.6M)
RiverRaid	8382.000000	6400000 (6.4M)

Requirements

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