Authors: Mawaba Pascal Dao, Adrian Peter
Abstract: Active Inference, grounded in the Free Energy Principle, provides a powerful
lens for understanding how agents balance exploration and goal-directed
behavior in uncertain environments. Here, we propose a new planning framework,
that integrates Monte Carlo Tree Search (MCTS) with active inference objectives
to systematically reduce epistemic uncertainty while pursuing extrinsic
rewards. Our key insight is that MCTS already renowned for its search
efficiency can be naturally extended to incorporate free energy minimization by
blending expected rewards with information gain. Concretely, the Cross-Entropy
Method (CEM) is used to optimize action proposals at the root node, while tree
expansions leverage reward modeling alongside intrinsic exploration bonuses.
This synergy allows our planner to maintain coherent estimates of value and
uncertainty throughout planning, without sacrificing computational
tractability. Empirically, we benchmark our planner on a diverse set of
continuous control tasks, where it demonstrates performance gains over both
standalone CEM and MCTS with random rollouts.
Source: http://arxiv.org/abs/2501.13083v1
