We present a method for transferring knowledge learned in one task to a related task. Our problem solvers employ reinforcement learning to acquire a model for one task. We then tra...
Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richa...
Bayesian inference is an appealing approach for leveraging prior knowledge in reinforcement learning (RL). In this paper we describe an algorithm for discovering different classes...
This paper examines, by argument, the dynamics of sequences of behavioural choices made, when non-cooperative restricted-memory agents learn in partially observable stochastic gam...
In this paper, we describe a conceptual framework and address the related issues and solutions in the identification of three major challenges for the development and evaluation of...
Effie Lai-Chong Law, Michael D. Kickmeier-Rust, Di...
This study aims to design a new co-evolution algorithm, Mixture Co-evolution which enables modeling of integration and composition of direct co-evolution and indirect coevolution....