In order to claim fully general intelligence in an autonomous agent, the ability to learn is one of the most central capabilities. Classical machine learning techniques have had ma...
This paper investigates the problem of automatically learning how to restructure the reward function of a Markov decision process so as to speed up reinforcement learning. We begi...
We introduce the ALeRT (Action-dependent Learning Rates with Trends) algorithm that makes two modifications to the learning rate and one change to the exploration rate of traditio...
Maria Cutumisu, Duane Szafron, Michael H. Bowling,...
We present the first large-scale empirical application of reinforcement learning to the important problem of optimized trade execution in modern financial markets. Our experiments...
A typical goal for transfer learning algorithms is to utilize knowledge gained in a source task to learn a target task faster. Recently introduced transfer methods in reinforcemen...