Multiagent reinforcement learning problems are especially difficult because of their dynamism and the size of joint state space. In this paper a new benchmark problem is proposed, ...
Agents in dynamic environments have to deal with world rep- To appear in: RoboCup 2005: Robot Soccer World Cup IX, c Springer-Verlag, 2006 resentations that change over time. In or...
Andreas D. Lattner, Andrea Miene, Ubbo Visser, Ott...
Many AI problems can be modeled as constraint satisfaction problems (CSP), but many of them are actually dynamic: the set of constraints to consider evolves because of the environ...
The existing reinforcement learning approaches have been suffering from the policy alternation of others in multiagent dynamic environments such as RoboCup competitions since othe...
Abstract. In [8] Yamauchi and Beer explored the abilities of continuous time recurrent neural networks (CTRNNs) to display reinforcementlearning like abilities. The investigated ta...