SimGAN：敵対的強化学習によるハイブリッドシミュレータ同定とドメイン適応
SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning
ニューラルネットと物理シミュレーションを組み合わせたハイブリッドシミュレータを敵対的強化学習で同定し、手動設計なしにsim-to-realなどのドメイン適応を実現するフレームワークを提案。
著者: Yifeng Jiang, Tingnan Zhang, Daniel Ho, Yunfei Bai, C. Karen Liu, Sergey Levine, Jie Tan
分類: cs.RO
原文アブストラクト
As learning-based approaches progress towards automating robot controllers design, transferring learned policies to new domains with different dynamics (e.g. sim-to-real transfer) still demands manual effort. This paper introduces SimGAN, a framework to tackle domain adaptation by identifying a hybrid physics simulator to match the simulated trajectories to the ones from the target domain, using a learned discriminative loss to address the limitations associated with manual loss design. Our hybrid simulator combines neural networks and traditional physics simulation to balance expressiveness and generalizability, and alleviates the need for a carefully selected parameter set in System ID. Once the hybrid simulator is identified via adversarial reinforcement learning, it can be used to refine policies for the target domain, without the need to interleave data collection and policy refinement. We show that our approach outperforms multiple strong baselines on six robotic locomotion tasks for domain adaptation.
