DR. JEFF BECK - THE BAYESIAN BRAIN
Machine Learning Street Talk (MLST)
Navigating Active Inference and Agent Dynamics
This chapter explores the concept of active inference, contrasting it with traditional reinforcement learning while emphasizing the importance of self-organization and emergent behavior in agents. It delves into the complexities of defining reward functions, advocating for homeostatic equilibrium to foster stability in agent modeling. The discussion further illuminates the dynamics of multi-agent systems and the potential innovations in machine learning architectures that could enhance cooperation and communication among artificial agents.
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