Many algorithms such as Q-learning successfully address reinforcement learning in single-agent multi-time-step problems. In addition there are methods that address reinforcement l...
actvoid.se Abstract. We describe how current work in Artificial Intelligence is using rigorous tools from information theory, namely information distance and experience distance to...
Chrystopher L. Nehaniv, Naeem Assif Mirza, Lars Ol...
Models of agent-environment interaction that use predictive state representations (PSRs) have mainly focused on the case of discrete observations and actions. The theory of discre...
We take the position that autonomous agents, when they interact with people, should be governed by the same principles that underlie human collaboration. These principles come fro...
Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is that...