arXiv:2606.04845v1 Announce Type: cross Abstract: Sequential decision-making problems are often modelled as a Markov decision process (MDP). We focus on the stochastic shortest path (SSP) problem, which is an infinite-horizon undiscounted MDP with absorbing terminal states. We develop a Bayesian framework to learn the optimal decision strategy through interactions with the decision-making task. Specifically, we learn the optimal action-value function $Q^*$, but unlike many existing Bayesian approaches, we do not rely on unrealistic modelling assumptions and ad-hoc approximations. Our approach
This paper leverages recent advancements in Bayesian learning and decision-making under uncertainty, building on established MDP frameworks to refine optimal strategy learning.
Improved Bayesian learning for sequential decision-making can lead to more robust and adaptive AI systems, particularly in agents operating in complex, uncertain environments.
The development of more refined Bayesian methods for learning optimal decision strategies reduces reliance on unrealistic assumptions in AI agent design, leading to more practical and reliable systems.
- · AI agents developers
- · Robotics and autonomous systems
- · Logistics and supply chain optimization
- · Developers relying solely on ad-hoc approximations
- · Systems with high uncertainty and brittle decision-making
More efficient and reliable AI agent behavior in real-world applications.
Accelerated development of autonomous systems capable of learning and adapting with less human intervention.
Increased integration of AI agents across various industries, enhancing automation and operational efficiency.
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