An overview of multi-agent reinforcement learning from game theoretical perspective
Y Yang, J Wang - arxiv preprint arxiv:2011.00583, 2020 - arxiv.org
Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
Cognitive radio: brain-empowered wireless communications
S Haykin - IEEE journal on selected areas in communications, 2005 - ieeexplore.ieee.org
Cognitive radio is viewed as a novel approach for improving the utilization of a precious
natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a …
natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a …
A unified game-theoretic approach to multiagent reinforcement learning
There has been a resurgence of interest in multiagent reinforcement learning (MARL), due
partly to the recent success of deep neural networks. The simplest form of MARL is …
partly to the recent success of deep neural networks. The simplest form of MARL is …
OpenSpiel: A framework for reinforcement learning in games
OpenSpiel is a collection of environments and algorithms for research in general
reinforcement learning and search/planning in games. OpenSpiel supports n-player (single …
reinforcement learning and search/planning in games. OpenSpiel supports n-player (single …
A survey on algorithms for Nash equilibria in finite normal-form games
Nash equilibrium is one of the most influential solution concepts in game theory. With the
development of computer science and artificial intelligence, there is an increasing demand …
development of computer science and artificial intelligence, there is an increasing demand …
Open-ended learning in symmetric zero-sum games
Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of
agents, for example labeling them 'winner'and 'loser'. If the game is approximately transitive …
agents, for example labeling them 'winner'and 'loser'. If the game is approximately transitive …
A unified approach to reinforcement learning, quantal response equilibria, and two-player zero-sum games
This work studies an algorithm, which we call magnetic mirror descent, that is inspired by
mirror descent and the non-Euclidean proximal gradient algorithm. Our contribution is …
mirror descent and the non-Euclidean proximal gradient algorithm. Our contribution is …
Towards unifying behavioral and response diversity for open-ended learning in zero-sum games
Measuring and promoting policy diversity is critical for solving games with strong non-
transitive dynamics where strategic cycles exist, and there is no consistent winner (eg, Rock …
transitive dynamics where strategic cycles exist, and there is no consistent winner (eg, Rock …
Modelling behavioural diversity for learning in open-ended games
Promoting behavioural diversity is critical for solving games with non-transitive dynamics
where strategic cycles exist, and there is no consistent winner (eg, Rock-Paper-Scissors) …
where strategic cycles exist, and there is no consistent winner (eg, Rock-Paper-Scissors) …
A generalized training approach for multiagent learning
This paper investigates a population-based training regime based on game-theoretic
principles called Policy-Spaced Response Oracles (PSRO). PSRO is general in the sense …
principles called Policy-Spaced Response Oracles (PSRO). PSRO is general in the sense …