成功訪問マッチングによるプロセス報酬学習でRLを効率化
Learning Process Rewards via Success Visitation Matching for Efficient RL
スパースな報酬を、成功・失敗エピソードを判別する識別器を使って密なプロセス報酬に変換し、ロボット操作タスクのRL微調整を高速化する手法を提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Raymond Tsao, Andrew Wagenmaker, Sergey Levine
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
In many modern applications of reinforcement learning (RL), the natural reward for a task of interest is inherently sparse: a reward of 0 is given everywhere except when the task is completed, when a reward of +1 is given. Training a policy to maximize such a sparse reward requires solving a challenging credit assignment problem, leading to slow or ineffective RL improvement. We propose a simple approach to transform a sparse outcome reward into a dense process reward. Our approach relies on training a discriminator to distinguish between previous successful and unsuccessful episodes, and using this discriminator to incentivize the RL-learned policy to match the state-action visitations of successful episodes, while avoiding those of unsuccessful episodes. By incentivizing the policy to match the visitations over all states, not just those that correspond to task success, this reward provides dense feedback on whether progress is being made towards task completion, and, we show, provably achieves this without changing the optimal policy. Focusing on finetuning of robotic control policies, we demonstrate that our approach leads to significantly faster RL finetuning performance on both simulated and real-world manipulation tasks, as compared to simply maximizing the sparse outcome reward.
