Reinforcement Learning with Subspaces using Free Energy Paradigm

Understand

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.