Active Inference and Reinforcement Learning: A unified inference on continuous state and action spaces under partial observability

TL;DR

This paper unifies active inference and reinforcement learning to improve decision-making in continuous, partially observable environments, enabling better exploration and learning without explicit reward signals.

Contribution

It establishes a theoretical connection between active inference and reinforcement learning, allowing their integration in continuous space POMDPs and overcoming previous computational limitations.

Findings

Demonstrates superior learning in continuous POMDP tasks.

Enables reward-free exploration through information-seeking behavior.

Shows theoretical and empirical advantages over traditional methods.

Abstract

Reinforcement learning (RL) has garnered significant attention for developing decision-making agents that aim to maximize rewards, specified by an external supervisor, within fully observable environments. However, many real-world problems involve partial observations, formulated as partially observable Markov decision processes (POMDPs). Previous studies have tackled RL in POMDPs by either incorporating the memory of past actions and observations or by inferring the true state of the environment from observed data. However, aggregating observed data over time becomes impractical in continuous spaces. Moreover, inference-based RL approaches often require many samples to perform well, as they focus solely on reward maximization and neglect uncertainty in the inferred state. Active inference (AIF) is a framework formulated in POMDPs and directs agents to select actions by minimizing a function called expected free energy (EFE). This supplies reward-maximizing (exploitative) behaviour, as in RL, with information-seeking (exploratory) behaviour. Despite this exploratory behaviour of AIF, its usage is limited to discrete spaces due to the computational challenges associated with EFE. In this paper, we propose a unified principle that establishes a theoretical connection between AIF and RL, enabling seamless integration of these two approaches and overcoming their aforementioned limitations in continuous space POMDP settings. We substantiate our findings with theoretical analysis, providing novel perspectives for utilizing AIF in the design of artificial agents. Experimental results demonstrate the superior learning capabilities of our method in solving continuous space partially observable tasks. Notably, our approach harnesses information-seeking exploration, enabling it to effectively solve reward-free problems and rendering explicit task reward design by an external supervisor optional.