AI RESEARCH
Bridging the Gap Between Average and Discounted TD Learning
arXiv CS.LG
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ArXi:2605.02103v1 Announce Type: new The analysis of Temporal Difference (TD) learning in the average-reward setting faces notable theoretical difficulties because the Bellman operator is not contractive with respect to any norm. This complicates standard analyses of stochastic updates that are effective in discounted settings. Although a considerable body of literature addresses these challenges, existing theoretical approaches come with limitations. We