Simple and Optimal Methods for Stochastic Variational Inequalities, II: Markovian Noise and Policy Evaluation in Reinforcement Learning

Iterative Methods for Equilibrium Problems, Variational Inequalities and Fixed Points

Variational methods for Reinforcement Learning

We consider reinforcement learning as solving a Markov decision process with unknown transition distribution. Based on interaction with the environment, an estimate of the transition matrix is obtained from which the optimal decision policy is formed. The classical maximum likelihood point estimate of the transition model does not reflect the uncertainty in the estimate of the transition model ...

Strong convergence for variational inequalities and equilibrium problems and representations

We introduce an implicit method for nding a common element of the set of solutions of systems of equilibrium problems and the set of common xed points of a sequence of nonexpansive mappings and a representation of nonexpansive mappings. Then we prove the strong convergence of the proposed implicit schemes to the unique solution of a variational inequality, which is the optimality condition for ...

designing and validating a textbook evaluation questionnaire for reading comprehension ii and exploring its relationship with achievement

در هر برنامه آموزشی، مهم ترین فاکتور موثر بر موفقیت دانش آموزان کتاب درسی است (مک دونو و شاو 2003). در حقیقت ، کتاب قلب آموزش زبان انگلیسی است( شلدن 1988). به دلیل اهمیت والای کتاب به عنوان عنصر ضروری کلاس های آموزش زبان ، کتب باید به دقت ارزیابی و انتخاب شده تا از هرگونه تاثیر منفی بر دانش آموزان جلوگیری شود( لیتز). این تحقیق با طراحی پرسش نامه ارزیابی کتاب که فرصت ارزیابی معتبر را به اساتید د...

Optimal policy switching algorithms for reinforcement learning

We address the problem of single-agent, autonomous sequential decision making. We assume that some controllers or behavior policies are given as prior knowledge, and the task of the agent is to learn how to switch between these policies. We formulate the problem using the framework of reinforcement learning and options (Sutton, Precup & Singh, 1999; Precup, 2000). We derive gradient-based algor...