最大エントロピー多タスク逆強化学習
Maximum Entropy Multi-Task Inverse RL
最大エントロピー逆強化学習とディリクレ過程によるクラスタリングを組み合わせ、専門家が複数の方策を切り替える状況でも複数の報酬関数を学習できる手法を提案し、玉ねぎ選別ロボットタスクで有効性を示した。
著者: Saurabh Arora, Bikramjit Banerjee, Prashant Doshi
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
Multi-task IRL allows for the possibility that the expert could be switching between multiple ways of solving the same problem, or interleaving demonstrations of multiple tasks. The learner aims to learn the multiple reward functions that guide these ways of solving the problem. We present a new method for multi-task IRL that generalizes the well-known maximum entropy approach to IRL by combining it with the Dirichlet process based clustering of the observed input. This yields a single nonlinear optimization problem, called MaxEnt Multi-task IRL, which can be solved using the Lagrangian relaxation and gradient descent methods. We evaluate MaxEnt Multi-task IRL in simulation on the robotic task of sorting onions on a processing line where the expert utilizes multiple ways of detecting and removing blemished onions. The method is able to learn the underlying reward functions to a high level of accuracy and it improves on the previous approaches to multi-task IRL.
