A Robust Model-Based Approach for Continuous-Time Policy Evaluation with Unknown L\'evy Process Dynamics
arXiv:2504.01482v3 Announce Type: replace-cross Abstract: This paper develops a model-based framework for continuous-time policy evaluation (CTPE) in reinforcement learning, incorporating both Brownian and L\'evy noise to model stochastic dynamics influenced by rare and extreme events. Our approach formulates the policy evaluation problem as solving a partial integro-differential equation (PIDE) for the value function with unknown coefficients. A key challenge in this setting is accurately recovering the unknown coefficients in the stochastic dynamics, particularly when driven by L\'evy proces
This research is published as reinforcement learning increasingly confronts real-world complexities, where simplified stochastic models are insufficient for robust policy evaluation, especially in dynamic environments with rare, high-impact events.
Improved continuous-time policy evaluation, particularly with Lèvy processes, enables more robust and reliable AI systems to operate in complex, unpredictable environments, a critical step for autonomous agents and control systems.
The ability to accurately model and evaluate policies under unknown Lèvy process dynamics signifies a step change in developing AI that can internalize and respond to 'black swan' type events, moving beyond Brownian motion assumptions.
- · AI/ML researchers
- · Autonomous systems developers
- · Financial modeling firms
- · Healthcare and logistics
- · AI systems relying solely on Gaussian assumptions
- · Systems unprepared for extreme events
More resilient AI agents capable of operating in highly stochastic environments, including those with fat-tailed distributions.
Accelerated development of autonomous systems across various industries due to enhanced reliability and safety in unpredictable conditions.
Potential for AI to manage complex, previously intractable systems like national power grids or supply chains with greater efficiency and robustness.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG