2017/03/19 by Peeyush Kumar, Doina Precup, Kumar, Peeyush +1
Computer Science · Physics and Astronomy · #Advanced Vision and Imaging #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Human Pose and Action Recognition #Model Reduction and Neural Networks
paper · pdf · doi:10.48550/arxiv.1703.06471
openalex publication_date 2017/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Deliberating on large or continuous state spaces have been long standing challenges in reinforcement learning. Temporal Abstraction have somewhat made this possible, but efficiently planing using temporal abstraction still remains an issue. Moreover using spatial abstractions to learn policies for various situations at once while using temporal abstraction models is an open problem. We propose here an efficient algorithm which is convergent under linear function approximation while planning using temporally abstract actions. We show how this algorithm can be used along with randomly generated option models over multiple time scales to plan agents which need to act real time. Using these randomly generated option models over multiple time scales are shown to reduce number of decision epochs required to solve the given task, hence effectively reducing the time needed for deliberation.