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This paper introduces a novel method for learning how to play the mostdifficult Atari 2600 games from the Arcade Learning Environment using deepreinforcement learning. The proposed method, human checkpoint replay, consistsin using checkpoints sampled from human gameplay as starting points for thelearning process. This is meant to compensate for the difficulties of currentexploration strategies, such as epsilon-greedy, to find successful controlpolicies in games with sparse rewards. Like other deep reinforcement learningarchitectures, our model uses a convolutional neural network that receives onlyraw pixel inputs to estimate the state value function. We tested our method onMontezuma's Revenge and Private Eye, two of the most challenging games from theAtari platform. The results we obtained show a substantial improvement comparedto previous learning approaches, as well as over a random player. We alsopropose a method for training deep reinforcement learning agents using humangameplay experience, which we call human experience replay.

Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay