arXiv AI
Oct 31

Safe Multi-Objective Reinforcement Learning: Zero Constraint Violation and Primal-Averaging Convergence

The paper introduces two algorithms for safe multi‑objective reinforcement learning that guarantee zero constraint violation during learning. The first algorithm learns a safe Pareto‑optimal policy for a given preference by combining optimistic reward estimates with pessimistic cost bounds, achieving an ×O(1/√T) suboptimality gap. The second, preference‑free algorithm safely explores first and then computes a safe policy for any preference, retaining the same safety guarantee and achieving an ×O(1/√T+1/√K) suboptimality gap.

By Chenxi Li, Yao Shu, Siyang Gao, Shuang Qiu, Guangxin Jiang
arXiv Machine Learning
Sep 30

Global Optimality for Constrained Exploration via Penalty Regularization

The paper introduces Policy Gradient Penalty (PGP), a single‑loop policy‑space method that enforces convex occupancy‑measure constraints via quadratic‑penalty regularization. PGP constructs pseudo‑rewards to estimate gradients of the penalized objective and uses the classical Policy Gradient Theorem, establishing smoothness and global last‑iterate convergence guarantees for an ε‑optimal constrained entropy value with ε‑bounded constraint violation. The authors validate PGP with ablations on a grid‑world benchmark and demonstrate scalability on two challenging continuous‑control tasks.

By Florian Wolf, Ilyas Fatkhullin, Niao He
arXiv Machine Learning
Sep 3

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.

By Jiaming Zhang, Yujie Yang, Haoning Wang, Liping Zhang, Shengbo Eben Li