Trust Region Q Adjoint Matching
Overview
TRQAM improves off-policy RL stability by controlling path-space KL divergence, achieving 68% success in offline RL, surpassing prior methods by 22% on 50 OGBench tasks.,
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- arXiv Robotics — research abstractsTrust Region Q Adjoint Matching for Stable Off-Policy RL
TRQAM improves off-policy reinforcement learning stability by controlling path-space KL divergence via a trust-region parameter. It outperforms prior methods with 68% success in offline RL.
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