情報理論的モデル予測Q学習
Information Theoretic Model Predictive Q-Learning
情報理論的MPCとエントロピー正則化強化学習の理論的関連を示し、バイアスのあるモデルを活用できるQ学習アルゴリズムを提案した論文。
著者: Mohak Bhardwaj, Ankur Handa, Dieter Fox, Byron Boots
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
Model-free Reinforcement Learning (RL) works well when experience can be collected cheaply and model-based RL is effective when system dynamics can be modeled accurately. However, both assumptions can be violated in real world problems such as robotics, where querying the system can be expensive and real-world dynamics can be difficult to model. In contrast to RL, Model Predictive Control (MPC) algorithms use a simulator to optimize a simple policy class online, constructing a closed-loop controller that can effectively contend with real-world dynamics. MPC performance is usually limited by factors such as model bias and the limited horizon of optimization. In this work, we present a novel theoretical connection between information theoretic MPC and entropy regularized RL and develop a Q-learning algorithm that can leverage biased models. We validate the proposed algorithm on sim-to-sim control tasks to demonstrate the improvements over optimal control and reinforcement learning from scratch. Our approach paves the way for deploying reinforcement learning algorithms on real systems in a systematic manner.
