Reinforcement Learning with Subspaces using Free Energy Paradigm

Milad Ghorbani; Reshad Hosseini; Seyed Pooya Shariatpanahi; Majid Nili Ahmadabadi

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arXiv · 2012.07091

Reinforcement Learning with Subspaces using Free Energy Paradigm

Milad Ghorbani·Reshad Hosseini·Seyed Pooya Shariatpanahi·Majid Nili Ahmadabadi

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Abstract

In large-scale problems, standard reinforcement learning algorithms suffer from slow learning speed. In this paper, we follow the framework of using subspaces to tackle this problem. We propose a free-energy minimization framework for selecting the subspaces and integrate the policy of the state-space into the subspaces. Our proposed free-energy minimization framework rests upon Thompson sampling policy and behavioral policy of subspaces and the state-space. It is therefore applicable to a variety of tasks, discrete or continuous state space, model-free and model-based tasks. Through a set of experiments, we show that this general framework highly improves the learning speed. We also provide a convergence proof.

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BibTeX RIS

Milad Ghorbani, Reshad Hosseini, Seyed Pooya Shariatpanahi, Majid Nili Ahmadabadi. 2020-12-13. Reinforcement Learning with Subspaces using Free Energy Paradigm. https://arxiv.org/abs/2012.07091

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