| We present a neural eld approach to distributed Q-learning in continuous state and action spaces that is based on action coding and selection in dynamic neural elds. It is, to the best of our knowledge, one of the rst attempts that combines the advantages of a topological action coding with a distributed action-value learning in one neural architecture. This combination, supplemented by a neu...

Many studies of learning have explored the impact of learning within organizations. This paper examines the impact of organization on learning. Organizational development interventions rarely pay attention to the emotional and political dynamics of organizational learning, even though the way people organize learning is intricately interwoven with emotion and politics. The aim of this paper is ...

We investigate the learning problem in stochastic games with continuous action spaces. We focus on repeated normal form games, and discuss issues in modelling mixed strategies and adapting learning algorithms in finite-action games to the continuous-action domain. We applied variable resolution techniques to two simple multi-agent reinforcement learning algorithms PHC and MinimaxQ. Preliminary ...