収束する監督者からのオンポリシー模倣学習
On-Policy Robot Imitation Learning from a Converging Supervisor
学習中に性能が向上していく監督者からの模倣学習を定式化し、収束後の最良方策に対する後悔保証を示すとともに、モデルベースRLの監督者と組み合わせることで連続制御タスクでの高速化と性能向上を実現した。
著者: Ashwin Balakrishna, Brijen Thananjeyan, Jonathan Lee, Felix Li, Arsh Zahed, Joseph E. Gonzalez, Ken Goldberg
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Existing on-policy imitation learning algorithms, such as DAgger, assume access to a fixed supervisor. However, there are many settings where the supervisor may evolve during policy learning, such as a human performing a novel task or an improving algorithmic controller. We formalize imitation learning from a "converging supervisor" and provide sublinear static and dynamic regret guarantees against the best policy in hindsight with labels from the converged supervisor, even when labels during learning are only from intermediate supervisors. We then show that this framework is closely connected to a class of reinforcement learning (RL) algorithms known as dual policy iteration (DPI), which alternate between training a reactive learner with imitation learning and a model-based supervisor with data from the learner. Experiments suggest that when this framework is applied with the state-of-the-art deep model-based RL algorithm PETS as an improving supervisor, it outperforms deep RL baselines on continuous control tasks and provides up to an 80-fold speedup in policy evaluation.
