Active Inference and Reinforcement Learning: A Unified Inference on Continuous State and Action Spaces Under Partial Observability
Parvin Malekzadeh,
Konstantinos N. Plataniotis
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 or noisy observations, where agents cannot access complete and accurate information about the environment. These problems are commonly formulated as partially observable Markov decision processes (POMDPs). Previous studies have tackled RL in POMDPs by either … Show more
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