Reinforcement learning and neural architecture for behavioural homeostasis

Abstract

Homeostasis is a fundamental property of animals that maintains the body's internal state. Homeostatic reinforcement learning (homeostatic RL) has been used to study how behaviors emerge from homeostasis, but previous studies have been limited to small-scale problems. This study focuses on scaling up homeostatic RL to enable the emergence of behaviors in high-dimensional input and continuous motor control. Deep RL is applied to the homeostatic RL domain, and the most effective reward setting is identified. An attention mechanism is also incorporated into the policy network structure to facilitate learning of appropriate behavior based on the body's internal state. This work provides insights into how homeostasis can be used to explain animal behavior and how homeostatic RL can be applied to more complex problems.