Presents the first results in understanding the reasons for cooperative advantage between
reinforcement learning agents. We consider a cooperation method which consists of using …

We consider a pseudo-realistic world in which one or more opportunities appear and
disappear in random locations. Agents use fuzzy reinforcement learning to learn which …

Solving multiagent reinforcement learning problems is a key issue. Indeed, the complexity of
deriving multiagent plans, especially when one uses an explicit model of the problem, is …

This paper provides the first convergence proof for fuzzy reinforcement learning. We extend
the work of Konda and Tsitsiklis (2000), who presented a convergent actor-critic algorithm …

In a multi-agent environment, whether one agent's action is good or not depends on the
other agents' actions. In traditional reinforcement learning methods, which assume …

This paper presents a cooperative reinforcement learning algorithm of multi-agent systems.
The cooperative behaviour is established within a leader-following framework. Specifically …

Realization of cooperative behavior in multi-agent system is important for improving problem
solving ability. Reinforcement learning is one of the learning methods for such cooperative …

The behavior of agents in complex and dynamic environments cannot be programmed a
priori, but needs to self-adapt to the specific situations. We present some approaches based …

We examine the dynamics of multiple reinforcement learning agents who are interacting with
and learning from the same environment in parallel. Due to the stochasticity of the …

GARIC-Q, introduced earlier by Berenji (1996), performs incremental dynamic programming
using intelligent agents which are controlled at the top level by fuzzy Q-learning and at the …