A monotone semi-discrete policy iteration scheme with O(h) artificial viscosity for stationary discounted HJB equations converges geometrically for fixed h and achieves O(sqrt(h)) error to the viscosity solution.
Markov decision processes.Handbooks in operations research and management science, 2:331–434
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Target-Aligned Bellman Backup (TABB) improves cross-domain offline RL by selecting source transitions according to their contribution to accurate target-domain Bellman target estimation.
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Policy Iteration for Stationary Discounted Hamilton--Jacobi--Bellman Equations: A Viscosity Approach
A monotone semi-discrete policy iteration scheme with O(h) artificial viscosity for stationary discounted HJB equations converges geometrically for fixed h and achieves O(sqrt(h)) error to the viscosity solution.
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Target-Aligned Bellman Backup for Cross-domain Offline Reinforcement Learning
Target-Aligned Bellman Backup (TABB) improves cross-domain offline RL by selecting source transitions according to their contribution to accurate target-domain Bellman target estimation.