Evolving Robustness--Exploration Trade-off in Online Reinforcement Learning via Quantile Bayesian Risk MDPs
arXiv:2605.24345v1 Announce Type: new Abstract: In online reinforcement learning, data scarcity creates epistemic uncertainty that makes robustness important early in learning, whereas sufficient exploration is needed to learn the true-environment optimal policy. We study this time-varying robustness--exploration trade-off through a quantile Bayesian risk-aware Markov decision process (BR-MDP), in which the quantile level controls how posterior uncertainty enters the Bellman backup. We characterize this control through an asymptotic normality result for the difference between the quantile BR-M
The increasing complexity and real-world application of AI systems necessitate more robust and adaptive learning approaches to handle uncertainty effectively.
Improving the robustness and exploration trade-off in online reinforcement learning is critical for developing more reliable and autonomous AI agents capable of operating in dynamic and uncertain environments.
This research provides a theoretical framework (quantile Bayesian risk-aware MDPs) to better manage epistemic uncertainty during AI's learning phase, potentially leading to more stable and adaptable AI-driven solutions.
- · AI agents developers
- · Robotics companies
- · Autonomous systems sector
- · Logistics and supply chain optimization
More reliable and less error-prone AI systems in real-world deployment.
Accelerated adoption of AI in high-stakes environments due to increased trust in their robustness.
New regulatory frameworks may emerge to certify AI robustness based on such theoretical advancements.
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Read at arXiv cs.LG