arXiv AI
Provably Safe Sim-to-Real Transfer
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Sampling-Based Safe Reinforcement Learning
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arXiv Machine Learning
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An Real-Sim-Real (RSR) Loop Framework for Generalizable Robotic Policy Transfer
arXiv:2503.10118v3 Announce Type: replace-cross Abstract: The sim-to-real gap remains a critical challenge in robotics, hindering the deployment of algorithms trained in simulation to real-world syst...
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Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
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Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning
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arXiv AI
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Safe Exploration via Policy Priors
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Exchange Policy Optimization Algorithm for Semi-Infinite Safe Reinforcement Learning
The paper introduces Exchange Policy Optimization (EPO), a framework for semi‑infinite safe reinforcement learning that handles infinitely many constraints by iteratively solving finite subproblems. EPO expands or deletes constraints based on tolerance violations and Lagrange multipliers, maintaining computational tractability while converging to an optimal policy with bounded safety violations. The authors prove finite convergence, provide iteration bounds, and quantify the suboptimality gap under mild assumptions.