Human-like recall/association for transfer learning in reinforcement learning (2020)
Knowledge transfer is a feature present in the learning process of multiple animal species that machine learning algorithms are capable of imitating. Although different transfer techniques have been developed in reinforcement learning, few techniques have focused on the reproduction of the memory units involved in knowledge transfer performed by mammals, particularly the episodic memory of humans. This dissertation presents a method that facilitates the transfer by means of a memory unit when an agent is learning to solve an unknown task that is more difficult than previously learned tasks. This dissertation focuses on developing a methodology that integrates characteristics of human learning, in terms of the structures or systems involved, in the context of reinforcement learning to make better use of the information derived from the interaction with environments in past tasks. We show that the memory unit can be learned autonomously over a space in which situations are related through sequences of states that represent the experience of the agent. This space gives the agent a broader context for efficiently relating past experiences and determining the moments when the transfer should occur while learning a new task.