The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Gradient descent for general reinforcement learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Convergent Gradient Ascent in General-Sum Games
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Friend-or-Foe Q-learning in General-Sum Games
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-Agent Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
If multi-agent learning is the answer, what is the question?
Artificial Intelligence
An economist's perspective on multi-agent learning
Artificial Intelligence
Agendas for multi-agent learning
Artificial Intelligence
Multiagent learning is not the answer. It is the question
Artificial Intelligence
What evolutionary game theory tells us about multiagent learning
Artificial Intelligence
A hierarchy of prescriptive goals for multiagent learning
Artificial Intelligence
Multi-agent learning and the descriptive value of simple models
Artificial Intelligence
A spiking neural network model of an actor-critic learning agent
Neural Computation
Rational and convergent learning in stochastic games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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This paper investigates Multiagent Reinforcement Learning (MARL) in a general-sum game where the payoffs' structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory nature of these games makes their study in multiagent systems quite challenging. In particular, we investigate MARL with spiking and non-spiking agents in the Iterated Prisoner's Dilemma by exploring the conditions required to enhance its cooperative outcome. According to the results, this is enhanced by: (i) a mixture of positive and negative payoff values and a high discount factor in the case of non-spiking agents and (ii) having longer eligibility trace time constant in the case of spiking agents. Moreover, it is shown that spiking and non-spiking agents have similar behaviour and therefore they can equally well be used in any multiagent interaction setting. For training the spiking agents, a novel and necessary modification enhances competition to an existing learning rule based on stochastic synaptic transmission.