Mixing Human Demonstrations with Self-Exploration in Experience Replay for Deep Reinforcement Learning

Dylan Klein; Akansel Cosgun

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arXiv · 2107.06840

Mixing Human Demonstrations with Self-Exploration in Experience Replay for Deep Reinforcement Learning

Dylan Klein·Akansel Cosgun

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Abstract

We investigate the effect of using human demonstration data in the replay buffer for Deep Reinforcement Learning. We use a policy gradient method with a modified experience replay buffer where a human demonstration experience is sampled with a given probability. We analyze different ratios of using demonstration data in a task where an agent attempts to reach a goal while avoiding obstacles. Our results suggest that while the agents trained by pure self-exploration and pure demonstration had similar success rates, the pure demonstration model converged faster to solutions with less number of steps.

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Dylan Klein, Akansel Cosgun. 2021-07-14. Mixing Human Demonstrations with Self-Exploration in Experience Replay for Deep Reinforcement Learning. https://arxiv.org/abs/2107.06840

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