2018/07/09 by Hugo Penedones, Penedones, Hugo, Damien Vincent +11 · 1 citation
Computer Science · Mathematics · Social Sciences · #Electoral Systems and Political Participation #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Markov Chains and Monte Carlo Methods #Privacy-Preserving Technologies in Data #Reinforcement Learning in Robotics #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1807.03064
arxiv created 2018/07/09 · openalex publication_date 2018/07/09 · arxiv updated 2018/07/10 · openalex created_date 2022/08/04 · openalex updated_date 2026/07/28
Temporal-Difference learning (TD) [Sutton, 1988] with function approximation can converge to solutions that are worse than those obtained by Monte-Carlo regression, even in the simple case of on-policy evaluation. To increase our understanding of the problem, we investigate the issue of approximation errors in areas of sharp discontinuities of the value function being further propagated by bootstrap updates. We show empirical evidence of this leakage propagation, and show analytically that it must occur, in a simple Markov chain, when function approximation errors are present. For reversible policies, the result can be interpreted as the tension between two terms of the loss function that TD minimises, as recently described by [Ollivier, 2018]. We show that the upper bounds from [Tsitsiklis and Van Roy, 1997] hold, but they do not imply that leakage propagation occurs and under what conditions. Finally, we test whether the problem could be mitigated with a better state representation, and whether it can be learned in an unsupervised manner, without rewards or privileged information.