Reward Maximization Through Discrete Active Inference.

Area of Science:

• Computational neuroscience
• Artificial intelligence
• Reinforcement learning

Background:

• Active inference is a probabilistic framework for agent behavior modeling, based on minimizing free energy.
• It has shown success in reward maximization tasks, rivaling other methods.
• Understanding its connection to reward maximization is crucial for agent design.

Purpose of the Study:

• To clarify the conditions under which active inference agents perform reward-maximizing actions.
• To demonstrate how active inference relates to the Bellman equation in reinforcement learning.
• To compare standard and recursive active inference schemes for optimal control.

Main Methods:

• Analysis of active inference under partially observed Markov decision processes.
• Demonstration of conditions for Bellman optimality in active inference.
• Comparison of standard active inference with a recursive (sophisticated inference) scheme.

Main Results:

• Standard active inference yields Bellman optimal actions only for a planning horizon of 1.
• Recursive active inference can achieve Bellman optimal actions for any finite temporal horizon.
• The study elucidates the conditions for active inference to produce reward-optimal behavior.

Conclusions:

• Active inference can be reconciled with reward maximization principles.
• Recursive active inference offers a more general solution for optimal control compared to standard active inference.
• This work bridges active inference and reinforcement learning, highlighting their interconnectedness.