Hindsight Curriculum Generation Based Multi-Goal Experience Replay

Xiaoyun Feng

Keywords: reinforcement learning, multi-goal task, experience replay

Abstract: In multi-goal tasks with sparse rewards, it is challenging to learn from tons of experiences with zero rewards. Hindsight experience replay (HER), which replays past experiences with additional heuristic goals, has shown it possible for off-policy reinforcement learning (RL) to make use of failed experiences. However, the replayed experiences may not lead to well-explored state-action pairs, especially for a pseudo goal, which instead results in a poor estimate of the value function. To tackle the problem, we propose to resample hindsight experiences based on their likelihood under the current policy and the overall distribution. Based on the hindsight strategy, we introduce a novel multi-goal experience replay method that automatically generates a training curriculum, namely Hindsight Curriculum Generation (HCG). As the range of experiences expands, the generated curriculum strikes a dynamic balance between exploiting and exploring. We implement HCG with the vanilla Deep Deterministic Policy Gradient(DDPG), and experiments on several tasks with sparse binary rewards demonstrate that HCG improves sample efficiency of the state of the art.

One-sentence Summary: In this paper, we propose a novel multi-goal experience replay method HCG to sample hindsight experiences, and generate a training curriculum to guide the learning.

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