PlanningEFEMix: Hybrid Active Inference for Sequential Decision-Making under Uncertainty

Sequential decision-making under uncertainty remains a key challenge in artificial intelligence, especially in environments marked by partial observability and noisy feedback. While reinforcement learning and probabilistic planning have achieved significant success, each approach has limitations when used alone, including instability under noisy conditions and reliance on accurate generative models. Active Inference offers a principled alternative by framing perception, learning, and action selection as the minimization of Expected Free Energy, unifying exploration and goaldirected behavior within a Bayesian framework. This paper introduces PlanningEFEMix, a hybrid decision-making algorithm that enables meta-level planning across diverse inference agents using Expected Free Energy as a shared objective. The framework combines deterministic Active Inference, POMDP-based belief updating, contrastive learning, and model-free reinforcement learning within a single planning loop. Candidate actions are assessed via forward simulation across agents and selected via a softmax policy augmented with an adaptive, statedependent bias memory that incorporates experiential feedback. An experimental evaluation on a noisy preference inference benchmark shows improved robustness and stability compared to single-agent baselines, confirming the effectiveness of hybrid Active Inference planning under uncertainty.