We present a general methodology to automate the search for equilibrium strategies in games derived from computational experimentation. Our approach interleaves empirical game-the...
Reinforcement learning is an effective technique for learning action policies in discrete stochastic environments, but its efficiency can decay exponentially with the size of the ...
In this paper, we first discuss the meaning of physical embodiment and the complexity of the environment in the context of multi-agent learning. We then propose a vision-based rei...
Modeling learning agents in the context of Multi-agent Systems requires an adequate understanding of their dynamic behaviour. Usually, these agents are modeled similar to the diļ¬...
Decentralized reinforcement learning (DRL) has been applied to a number of distributed applications. However, one of the main challenges faced by DRL is its convergence. Previous ...
Chongjie Zhang, Victor R. Lesser, Sherief Abdallah