Free-energy-based reinforcement learning in a partially observable environment

Free-energy-based reinforcement learning (FERL) can handle Markov decision processes (MDPs) with high-dimensional state spaces by approximating the state-action value function with the negative equilibrium free energy of a restricted Boltzmann machine (RBM). In this study, we extend the FERL framework to handle partially observable MDPs (POMDPs) by incorporating a recurrent neural network that learns a memory representation sufficient for predicting future observations and rewards. We demonstrate that the proposed method successfully solves POMDPs with high-dimensional observations without any prior knowledge of the environmental hidden states and dynamics. After learning, task structures are implicitly represented in the distributed activation patterns of hidden nodes of the RBM.