arXiv AI October 31, 2026 By Chenxi Li, Yao Shu, Siyang Gao, Shuang Qiu, Guangxin Jiang

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.

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