痕跡を残さず：安全で自律的な強化学習のためのリセット学習
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
順方向ポリシーとリセットポリシーを同時に学習し、非可逆状態に入る前に自動で安全に中断することで、人手によるリセットを大幅に削減する強化学習手法を提案した論文。
著者: Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine
分類: cs.LG, cs.RO
原文アブストラクト
Deep reinforcement learning algorithms can learn complex behavioral skills, but real-world application of these methods requires a large amount of experience to be collected by the agent. In practical settings, such as robotics, this involves repeatedly attempting a task, resetting the environment between each attempt. However, not all tasks are easily or automatically reversible. In practice, this learning process requires extensive human intervention. In this work, we propose an autonomous method for safe and efficient reinforcement learning that simultaneously learns a forward and reset policy, with the reset policy resetting the environment for a subsequent attempt. By learning a value function for the reset policy, we can automatically determine when the forward policy is about to enter a non-reversible state, providing for uncertainty-aware safety aborts. Our experiments illustrate that proper use of the reset policy can greatly reduce the number of manual resets required to learn a task, can reduce the number of unsafe actions that lead to non-reversible states, and can automatically induce a curriculum.
