A number of experimental studies have investigated whether cooperative behavior may emerge in multi-agent Qlearning. In some studies cooperative behavior did emerge, in others it did not. This paper provides a theoretical analysis of this issue. The analysis focuses on multi-agent Q-learning in iterated prisoner’s dilemmas. It is shown that under certain assumptions cooperative behavior may emerge when multi-agent Q-learning is applied in an iterated prisoner’s dilemma. An important consequence of the analysis is that multi-agent Q-learning may result in non-Nash behavior. It is found experimentally that the theoretical results presented in this paper are quite robust to violations of the underlying assumptions
QQ-learning is a reinforcement learning model from the field of artificial intelligence. We study th...
Dynamic noncooperative multiagent systems are systems where self-interested agents interact with eac...
We describe a generalized Q-learning type algorithm for reinforcement learning in competitive multi-...
A number of experimental studies have investigated whether cooperative behavior may emerge in multi-...
textabstractA number of experimental studies have investigated whether cooperative behavior may emer...
Reinforcement learning can provide a robust and natural means for agents to learn how to coordinate ...
One of the important issues in intelligent systems and robotics is to develop an efficient method to...
We present a conceptual framework for creating Qlearning-based algorithms that converge to optimal e...
ABSTRACT This work considers a stateless Q-learning agent in iterated Prisoner's Dilemma (PD). ...
Qlearning is a recent reinforcement learning RL algorithm that does not need a model of its environ...
We report on an investigation of reinforcement learning tech-niques for the learning of coordination...
We report on an investigation of reinforcement learning techniques for the learning of coordination...
Some game theory approaches to solve multiagent reinforce-ment learning in self play, i.e. when agen...
Evolution of cooperation and competition can appear when multiple adaptive agents share a biological...
Evolution of cooperation and competition can appear when multiple adaptive agents share a biological...
QQ-learning is a reinforcement learning model from the field of artificial intelligence. We study th...
Dynamic noncooperative multiagent systems are systems where self-interested agents interact with eac...
We describe a generalized Q-learning type algorithm for reinforcement learning in competitive multi-...
A number of experimental studies have investigated whether cooperative behavior may emerge in multi-...
textabstractA number of experimental studies have investigated whether cooperative behavior may emer...
Reinforcement learning can provide a robust and natural means for agents to learn how to coordinate ...
One of the important issues in intelligent systems and robotics is to develop an efficient method to...
We present a conceptual framework for creating Qlearning-based algorithms that converge to optimal e...
ABSTRACT This work considers a stateless Q-learning agent in iterated Prisoner's Dilemma (PD). ...
Qlearning is a recent reinforcement learning RL algorithm that does not need a model of its environ...
We report on an investigation of reinforcement learning tech-niques for the learning of coordination...
We report on an investigation of reinforcement learning techniques for the learning of coordination...
Some game theory approaches to solve multiagent reinforce-ment learning in self play, i.e. when agen...
Evolution of cooperation and competition can appear when multiple adaptive agents share a biological...
Evolution of cooperation and competition can appear when multiple adaptive agents share a biological...
QQ-learning is a reinforcement learning model from the field of artificial intelligence. We study th...
Dynamic noncooperative multiagent systems are systems where self-interested agents interact with eac...
We describe a generalized Q-learning type algorithm for reinforcement learning in competitive multi-...