Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes
arXiv:2512.14617v2 Announce Type: replace Abstract: Many practical decision-making problems involve tasks whose success depends on the entire system history, rather than on achieving a state with desired properties. Markovian Reinforcement Learning (RL) approaches are not suitable for such tasks, while RL with non-Markovian reward decision processes (NMRDPs) enables agents to tackle temporal-dependency tasks. This approach has long been known to lack formal guarantees on both (near-)optimality and sample efficiency. We contribute to solving both issues with QR-MAX, a novel model-based algorith
The continuous drive for more advanced AI autonomy necessitates robust theoretical and algorithmic foundations, especially as AI systems are deployed in complex, real-world scenarios requiring historical context.
Improving model-based reinforcement learning with formal guarantees for non-Markovian tasks opens doors for more reliable and capable autonomous AI systems, which are crucial for complex decision-making.
The development of algorithms like QR-MAX provides a pathway to address long-standing limitations in RL regarding non-Markovian reward decision processes, enhancing the practical applicability of AI agents.
- · AI developers
- · Robotics industry
- · Autonomous systems developers
- · Logistics and planning sectors
- · Developers of less robust, purely Markovian RL systems
More efficient and reliable AI agents can be developed for tasks requiring temporal-dependency understanding.
This advancement could accelerate the deployment of autonomous AI across various industries, replacing or augmenting human decision-making in complex operational environments.
The increased sophistication of AI decision-making could lead to new economic models and significant productivity gains in sectors currently limited by human cognitive bandwidth and error rates.
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