Stability and Sensitivity Analysis of Relative Temporal-Difference Learning: Extended Version
Relative temporal-difference (TD) learning was introduced to mitigate the slow convergence of TD methods when the discount factor approaches one by subtracting a baseline from the temporal-difference update. While this idea has been studied in the tabular setting, stability guarantees with function approximation remain poorly understood. This paper analyzes relative TD learning with linear function approximation. We establish stability conditions for the algorithm and show that the choice of baseline distribution plays a central role. In particular, when the baseline is chosen as the empirical distribution of the state-action process, the algorithm is stable for any non-negative baseline ...
| Authors | Masoud S. Sakha, Rushikesh Kamalapurkar, Sean Meyn |
|---|---|
| Primary category | cs.LG |
| Published | 29 March 2026 |
| Last updated at source | 7 April 2026 |
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