We introduce relational temporal difference learning as an effective approach to solving multi-agent Markov decision problems with large state spaces. Our algorithm uses temporal ...
A general game player is an agent capable of taking as input a description of a game’s rules in a formal language and proceeding to play without any subsequent human input. To do...
We review the application of statistical mechanics methods to the study of online learning of a drifting concept in the limit of large systems. The model where a feed-forward netwo...
We describe a system that successfully transfers value function knowledge across multiple subdomains of realtime strategy games in the context of multiagent reinforcement learning....
Classification of email is an important everyday task for a large and growing number of users. This paper describes the machine learning approaches underlying the i-ems (Intellige...