強化学習による接地行動変換を用いたsim2real転移
Reinforced Grounded Action Transformation for Sim-to-Real Transfer
シミュレータの現実ギャップを埋めるため、接地ステップ自体を強化学習で行うRGATを提案し、ニューラルネット方策のMuJoCoタスクでの実機転移に成功した。
著者: Haresh Karnan, Siddharth Desai, Josiah P. Hanna, Garrett Warnell, Peter Stone
分類: cs.RO
原文アブストラクト
Robots can learn to do complex tasks in simulation, but often, learned behaviors fail to transfer well to the real world due to simulator imperfections (the reality gap). Some existing solutions to this sim-to-real problem, such as Grounded Action Transformation (GAT), use a small amount of real-world experience to minimize the reality gap by grounding the simulator. While very effective in certain scenarios, GAT is not robust on problems that use complex function approximation techniques to model a policy. In this paper, we introduce Reinforced Grounded Action Transformation(RGAT), a new sim-to-real technique that uses Reinforcement Learning (RL) not only to update the target policy in simulation, but also to perform the grounding step itself. This novel formulation allows for end-to-end training during the grounding step, which, compared to GAT, produces a better grounded simulator. Moreover, we show experimentally in several MuJoCo domains that our approach leads to successful transfer for policies modeled using neural networks.
