Add to ORKGIn this paper we address the problem of coordination in multi-agent sequential decision problems with infinite state-spaces. We adopt a game theoretic formalism to describe the interaction of the multiple decision-makers and propose the novel approximate biased adaptive play algorithm. This al-gorithm is an extension of biased adaptive play to team Mar-kov games defined over infinite state-spaces. We establish our method to coordinate with probability 1 in the optimal strategy and discuss how this methodology can be combined with approximate learning architectures. We conclude with two simple examples of application of our algorithm
In this paper, we address the problem of convergence to Nash equilibria in games with rewards that a...
Consider a multi-agent system in a dynamic and uncertain environment. Each agent’s local decision pr...
We describe a generalized Q-learning type algorithm for reinforcement learning in competitive multi-...
In this paper, we address multi-agent decision problems where all agents share a common goal. This c...
We present a novel and uniform formulation of the problem of reinforcement learning against bounded ...
Agent competition and coordination are two classical and most important tasks in multiagent systems....
We present a new method for learning good strategies in zero-sum Markov games in which each side is...
We present a new method for learning good strategies in zero-sum Markov games in which each side is ...
This paper casts coordination of a team of robots within the framework of game theoretic learning al...
We represent the multiple pursuers and evaders game as a Markov game and each player as a decentrali...
This paper casts coordination of a team of robots within the framework of game theoretic learning al...
The multiple pursuers and evaders game may be represented as a Markov game. Using this modeling, one...
Stochastic games generalize Markov decision processes (MDPs) to a multiagent setting by allowing the...
Although multi-agent reinforcement learning can tackle systems of strategically interacting entities...
Fudenberg and Kreps consider adaptive learning processes, in the spirit of fictitious play, for inf...
In this paper, we address the problem of convergence to Nash equilibria in games with rewards that a...
Consider a multi-agent system in a dynamic and uncertain environment. Each agent’s local decision pr...
We describe a generalized Q-learning type algorithm for reinforcement learning in competitive multi-...
In this paper, we address multi-agent decision problems where all agents share a common goal. This c...
We present a novel and uniform formulation of the problem of reinforcement learning against bounded ...
Agent competition and coordination are two classical and most important tasks in multiagent systems....
We present a new method for learning good strategies in zero-sum Markov games in which each side is...
We present a new method for learning good strategies in zero-sum Markov games in which each side is ...
This paper casts coordination of a team of robots within the framework of game theoretic learning al...
We represent the multiple pursuers and evaders game as a Markov game and each player as a decentrali...
This paper casts coordination of a team of robots within the framework of game theoretic learning al...
The multiple pursuers and evaders game may be represented as a Markov game. Using this modeling, one...
Stochastic games generalize Markov decision processes (MDPs) to a multiagent setting by allowing the...
Although multi-agent reinforcement learning can tackle systems of strategically interacting entities...
Fudenberg and Kreps consider adaptive learning processes, in the spirit of fictitious play, for inf...
In this paper, we address the problem of convergence to Nash equilibria in games with rewards that a...
Consider a multi-agent system in a dynamic and uncertain environment. Each agent’s local decision pr...
We describe a generalized Q-learning type algorithm for reinforcement learning in competitive multi-...