Empirical evaluation methods for multiobjective reinforcement learning algorithms

Improved Empirical Methods in Reinforcement-learning Evaluation

OF THE DISSERTATION IMPROVED EMPIRICAL METHODS IN REINFORCEMENT-LEARNING EVALUATION by VUKOSI N. MARIVATE Dissertation Director: Michael L. Littman The central question addressed in this research is ”can we define evaluation methodologies that encourage reinforcement-learning (RL) algorithms to work effectively with real-life data?” First, we address the problem of overfitting. RL algorithms ar...

Offline Evaluation of Online Reinforcement Learning Algorithms

In many real-world reinforcement learning problems, we have access to an existing dataset and would like to use it to evaluate various learning approaches. Typically, one would prefer not to deploy a fixed policy, but rather an algorithm that learns to improve its behavior as it gains more experience. Therefore, we seek to evaluate how a proposed algorithm learns in our environment, meaning we ...

Algorithms for Reinforcement Learning

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner’s predictions. Further, the predictions may have long term effects through influ...

Insights in reinforcement rearning : formal analysis and empirical evaluation of temporal-difference learning algorithms

Evolutionary Algorithms for Reinforcement Learning

There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal di erence methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal di erence methods. This article focuses on the application of evo...