We use simulated soccer to study multiagent learning. Each team's players (agents) share action set and policy, but may behave di erently due to position-dependent inputs. All...
In order for an autonomous agent to behave robustly in a variety of environments, it must have the ability to learn approximations to many different functions. The function approx...
Autonomous state generalization problem is a key issue in the research field of behavior learning of reactive agents, and many approaches have been proposed in recent years. Howeve...
The generation of animated human figures especially in crowd scenes has many applications in such domains as the special effects industry, computer games or for the simulation of ...
Adam Szarowicz, Marek Mittmann, Paolo Remagnino, J...
A satisfactory multiagent learning algorithm should, at a minimum, learn to play optimally against stationary opponents and converge to a Nash equilibrium in self-play. The algori...