Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay

TL;DR

This paper presents human checkpoint replay, a new deep reinforcement learning method that uses human gameplay checkpoints to improve learning in challenging Atari games with sparse rewards.

Contribution

It introduces human checkpoint replay, a novel approach leveraging human gameplay data to enhance deep reinforcement learning in difficult environments.

Findings

Significant performance improvements on Montezuma's Revenge and Private Eye.

Outperforms previous methods and random play in challenging Atari games.

Proposes human experience replay for training deep RL agents.

Abstract

This paper introduces a novel method for learning how to play the most difficult Atari 2600 games from the Arcade Learning Environment using deep reinforcement learning. The proposed method, human checkpoint replay, consists in using checkpoints sampled from human gameplay as starting points for the learning process. This is meant to compensate for the difficulties of current exploration strategies, such as epsilon-greedy, to find successful control policies in games with sparse rewards. Like other deep reinforcement learning architectures, our model uses a convolutional neural network that receives only raw pixel inputs to estimate the state value function. We tested our method on Montezuma's Revenge and Private Eye, two of the most challenging games from the Atari platform. The results we obtained show a substantial improvement compared to previous learning approaches, as well as over a random player. We also propose a method for training deep reinforcement learning agents using human gameplay experience, which we call human experience replay.