効率的な近似推論による適応的確率的軌道最適化
Adaptive Probabilistic Trajectory Optimization via Efficient Approximate Inference
RLとMPCの利点を組み合わせ、スケーラブルな近似推論で確率的モデルをオンライン更新しながら局所近似で最適制御則を計算する手法を提案し、SSGPベースの2変種を3つの学習タスクで検証した。
著者: Yunpeng Pan, Xinyan Yan, Evangelos Theodorou, Byron Boots
分類: cs.RO, cs.LG
原文アブストラクト
Robotic systems must be able to quickly and robustly make decisions when operating in uncertain and dynamic environments. While Reinforcement Learning (RL) can be used to compute optimal policies with little prior knowledge about the environment, it suffers from slow convergence. An alternative approach is Model Predictive Control (MPC), which optimizes policies quickly, but also requires accurate models of the system dynamics and environment. In this paper we propose a new approach, adaptive probabilistic trajectory optimization, that combines the benefits of RL and MPC. Our method uses scalable approximate inference to learn and updates probabilistic models in an online incremental fashion while also computing optimal control policies via successive local approximations. We present two variations of our algorithm based on the Sparse Spectrum Gaussian Process (SSGP) model, and we test our algorithm on three learning tasks, demonstrating the effectiveness and efficiency of our approach.
