ガウス過程を用いた有限マルコフ決定過程における安全な探索
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
未知の安全制約下で有限MDPを安全に探索するアルゴリズムを提案し、安全到達可能領域を制約違反なく完全探索できることを証明した。ローバによる未知地図探索で実証した。
著者: Matteo Turchetta, Felix Berkenkamp, Andreas Krause
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
In classical reinforcement learning, when exploring an environment, agents accept arbitrary short term loss for long term gain. This is infeasible for safety critical applications, such as robotics, where even a single unsafe action may cause system failure. In this paper, we address the problem of safely exploring finite Markov decision processes (MDP). We define safety in terms of an, a priori unknown, safety constraint that depends on states and actions. We aim to explore the MDP under this constraint, assuming that the unknown function satisfies regularity conditions expressed via a Gaussian process prior. We develop a novel algorithm for this task and prove that it is able to completely explore the safely reachable part of the MDP without violating the safety constraint. To achieve this, it cautiously explores safe states and actions in order to gain statistical confidence about the safety of unvisited state-action pairs from noisy observations collected while navigating the environment. Moreover, the algorithm explicitly considers reachability when exploring the MDP, ensuring that it does not get stuck in any state with no safe way out. We demonstrate our method on digital terrain models for the task of exploring an unknown map with a rover.
