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[2602.15817] Solving Parameter-Robust Avoid Problems with Unknown Feasibility using Reinforcement Learning
Summary
This article presents a novel approach using Feasibility-Guided Exploration (FGE) to address parameter-robust avoid problems in reinforcement learning, enhancing policy performance in uncertain environments.
Why It Matters
The research addresses a critical gap in reinforcement learning applications by proposing a method that improves the safety and reliability of policies in dynamic environments. This is particularly relevant for robotics and control systems where ensuring safety is paramount.
Key Takeaways
- FGE enhances policy learning by identifying feasible initial conditions.
- The method outperforms existing approaches by over 50% in coverage.
- Addresses the mismatch between reachability and expected returns in RL.
- Empirical results demonstrate effectiveness in complex simulations.
- Contributes to safer reinforcement learning applications in robotics.
Computer Science > Machine Learning arXiv:2602.15817 (cs) [Submitted on 17 Feb 2026] Title:Solving Parameter-Robust Avoid Problems with Unknown Feasibility using Reinforcement Learning Authors:Oswin So, Eric Yang Yu, Songyuan Zhang, Matthew Cleaveland, Mitchell Black, Chuchu Fan View a PDF of the paper titled Solving Parameter-Robust Avoid Problems with Unknown Feasibility using Reinforcement Learning, by Oswin So and 5 other authors View PDF HTML (experimental) Abstract:Recent advances in deep reinforcement learning (RL) have achieved strong results on high-dimensional control tasks, but applying RL to reachability problems raises a fundamental mismatch: reachability seeks to maximize the set of states from which a system remains safe indefinitely, while RL optimizes expected returns over a user-specified distribution. This mismatch can result in policies that perform poorly on low-probability states that are still within the safe set. A natural alternative is to frame the problem as a robust optimization over a set of initial conditions that specify the initial state, dynamics and safe set, but whether this problem has a solution depends on the feasibility of the specified set, which is unknown a priori. We propose Feasibility-Guided Exploration (FGE), a method that simultaneously identifies a subset of feasible initial conditions under which a safe policy exists, and learns a policy to solve the reachability problem over this set of initial conditions. Empirical results...
