A Unified Bellman Operator for Safety-Critical Reinforcement Learning
Reinforcement learning in safety-critical domains requires maximizing task performance while strictly adhering to safety constraints. Existing safe reinforcement learning paradigms typically force a trade-off: they either require a priori knowledge to provide strict safety guarantees (e.g., safety filters), or they ena...
Nishanth Rao, Royina Karegoudra Jayanth, Benjamin Eysenbach et al.
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