Stein変分推論によるエントロピー正則化運動計画
Entropy Regularized Motion Planning via Stein Variational Inference
変分推論に基づくサンプリングで高自由度運動計画の実行可能かつ低コストな軌道分布を生成する手法を提案し、エントロピー正則化強化学習との関連を示した。
著者: Alexander Lambert, Byron Boots
分類: cs.RO
原文アブストラクト
Many Imitation and Reinforcement Learning approaches rely on the availability of expert-generated demonstrations for learning policies or value functions from data. Obtaining a reliable distribution of trajectories from motion planners is non-trivial, since it must broadly cover the space of states likely to be encountered during execution while also satisfying task-based constraints. We propose a sampling strategy based on variational inference to generate distributions of feasible, low-cost trajectories for high-dof motion planning tasks. This includes a distributed, particle-based motion planning algorithm which leverages a structured graphical representations for inference over multi-modal posterior distributions. We also make explicit connections to both approximate inference for trajectory optimization and entropy-regularized reinforcement learning.
