# Evolutionary game theory and multi-agent reinforcement learning

@article{Tuyls2005EvolutionaryGT, title={Evolutionary game theory and multi-agent reinforcement learning}, author={Karl Tuyls and Ann Now{\'e}}, journal={The Knowledge Engineering Review}, year={2005}, volume={20}, pages={63 - 90} }

In this paper we survey the basics of reinforcement learning and (evolutionary) game theory, applied to the field of multi-agent systems. [... ] Key Method Next we summarize the most important aspects of evolutionary game theory. Finally, we discuss the state-of-the-art of multi-agent reinforcement learning and the mathematical connection with evolutionary game theory. Expand

## 101 Citations

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- 2010

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- 2012

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## References

SHOWING 1-10 OF 112 REFERENCES

Towards a relation between learning agents and evolutionary dynamics

- Computer Science
- 2002

This paper examines whether evolutionary game theory, and more specifically the replicator dynamics, is an adequate theoretical model for the study of the dynamics of reinforcement learning agents in a multi-agent system.

Multiagent Reinforcement Learning in Stochastic Games

- Computer Science, EconomicsICML 1999
- 1999

This work designs a multiagent reinforcement learning method which allows agents to learn Nash equilibrium strategies and shows in both theory and experiments that this algorithm converges.

Extended Replicator Dynamics as a Key to Reinforcement Learning in Multi-agent Systems

- Computer ScienceECML
- 2003

This paper introduces an extension of the Replicator Dynamics from Evolutionary Game Theory and develops a Reinforcement Learning algorithm that attains a stable Nash equilibrium for all types of games.

A selection-mutation model for q-learning in multi-agent systems

- Computer ScienceAAMAS '03
- 2003

This work shows how the Replicator Dynamics (RD) can be used as a model for Q-learning in games and reveals an interesting connection between the exploitation-exploration scheme from RL and the selection-mutation mechanisms from evolutionary game theory.

On a Dynamical Analysis of Reinforcement Learning in Games: Emergence of Occam's Razor

- Economics, Computer ScienceCEEMAS
- 2003

This paper discusses three learning models whose dynamics are related to the Replicator Dynamics and shows how a classical Reinforcement Learning(RL) technique, i.e. Q-learning relates to the RD, allows to better understand the learning process and it allows to determine how complex a RL model should be.

Evolutionary Game Theory

- Economics
- 1994

This text introduces current evolutionary game theory--where ideas from evolutionary biology and rationalistic economics meet--emphasizing the links between static and dynamic approaches and…

Evolutionary Games and Equilibrium Selection

- Economics
- 1997

Evolutionary game theory is one of the most active and rapidly growing areas of research in economics. Unlike traditional game theory models, which assume that all players are fully rational and have…

Multiagent reinforcement learning in Markov games : asymmetric and symmetric approaches

- Computer Science
- 2004

Interactions between intelligent and rational agents are modeled with Markov games and the emphasis is on the adaptation and learning in multiagent systems.

Game theory and economics

- Economics
- 2003

Introduction to Game Theory and Outline of the Book Optimal Decentralised Decisions Non-Cooperative Games with Complete and Perfect Information Non-Cooperative Games with Imperfect or Incomplete…