WebMar 14, 2024 · This work proposes a simple modification to the classical policy-matching methods for regularizing with respect to the dual form of the Jensen–Shannon divergence and the integral probability metrics, and theoretically shows the correctness of the policy- matching approach. Highly Influenced PDF View 5 excerpts, cites methods WebProceedings of Machine Learning Research

Offline Reinforcement Learning Methods - Papers with Code

WebFeb 13, 2024 · Regularization methods reduce the divergence between the learned policy and the behavior policy, which may mismatch the inherent density-based definition of … http://proceedings.mlr.press/v139/wu21i/wu21i.pdf haviland electric heaters

Algorithms – Offline Reinforcement Learning Resources

WebOffline Reinforcement Learning with Fisher Divergence Critic Regularization. Many modern approaches to offline Reinforcement Learning (RL) utilize behavior … WebBehavior regularization then corresponds to an appropriate regularizer on the offset term. We propose using a gradient penalty regularizer for the offset term and demonstrate its equivalence to Fisher divergence regularization, suggesting connections to the score matching and generative energy-based model literature. WebBehavior regularization then corresponds to an appropriate regularizer on the offset term. We propose using a gradient penalty regularizer for the offset term and demonstrate its equivalence to Fisher divergence regularization, suggesting connections to the score matching and generative energy-based model literature. haviland dump hours