Exploration Strategies for Homeostatic Agents: Continuous and dynamic exploration for homeostatic regulation using deep reinforcement learning

Abstract

This paper introduces and evaluates four novel exploration strategies for homeostatic agents. Homeostatic agents have the objective of keeping some internal variables as close to a predetermined optimum as possible. Reinforcement learning is used for decision making, and the agents are given access to the optimal and acceptable values of the internal variables, giving greater flexibility for exploration and better survival chances. The new exploration strategies that utilise the internal variables are evaluated in a range of environments, showing them to outperform common reinforcement learning exploration techniques where these variables are not taken into consideration.