This paper suggests a routing method for automated guided vehicles in port terminals that uses the Q-learning technique. One of the most important issues for the efficient operati...
This paper investigates the challenges posed by the application of reinforcement learning to large-scale strategy games. In this context, we present steps and techniques which syn...
Charles A. G. Madeira, Vincent Corruble, Geber Ram...
We present a data-driven approach to learn user-adaptive referring expression generation (REG) policies for spoken dialogue systems. Referring expressions can be difficult to unde...
In the paper, we investigate the use of reinforcement learning in CBR for estimating and managing a legacy case base for playing the game of Tetris. Each case corresponds to a loc...
We present an expressive agent design language for reinforcement learning that allows the user to constrain the policies considered by the learning process.The language includes s...