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.

这篇文章提出了一种使用深度强化学习来学习玩最困难的Atari 2600游戏的新方法，即基于人类游戏经验的检查点回放，并使用卷积神经网络作为模型，其结果显著优于先前的学习方法和随机玩家，同时提出了一种使用人类游戏经验来训练深度强化学习智能体的方法。

使用深度强化学习和人类检查点重现玩雅达利游戏