AbstractGroup utility functions are an extension of the common team utility function for providing multiple agents with a common reinforcement learning signal for learning cooperat...
Designing agents whose behavior challenges human players adequately is a key issue in computer games development. This work presents a novel technique, based on reinforcement lear...
Gustavo Andrade, Geber Ramalho, Hugo Santana, Vinc...
We introduce an approach to autonomously creating state space abstractions for an online reinforcement learning agent using a relational representation. Our approach uses a tree-b...
In this paper the application of reinforcement learning to Tetris is investigated, particulary the idea of temporal difference learning is applied to estimate the state value funct...
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of comp...