Add to ORKGCopyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.We propose a new conceptual multi-agent framework which, given a game with an undesirable Nash equilibrium, will almost surely generate a new Nash equilibrium at some predetennined, more desirable pure action profile. The agent(s) targeted for reinforcement learn independently according to a standard model-free algorithm, using internally-generated states corresponding to high-level preference rankings over outcomes. We focus in particular on the case in which the additional reward can be considered as resulting from an internal (re-)appraisal, such that the new equilibrium is stable independent of the continued app...
Melioration learning is an empirically well-grounded model of reinforcement learning. By means of co...
We study the application of multi-agent reinforcement learning for game-theoretical problems. In par...
This paper focuses on a multi-agent cooperation which is generally difficult to be achieved without ...
Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents ’ ...
This paper seeks to establish a framework for directing a society of simple, specialized, self-inter...
Machine learning and artificial intelligence has been a hot topic the last few years, thanks to impr...
Repeated play in games by simple adaptive agents is investigated. The agents use Q-learning, a speci...
Learning in the real world occurs when an agent, which perceives its current state and takes actions...
Some game theory approaches to solve multiagent reinforce-ment learning in self play, i.e. when agen...
Recent models of learning in games have attempted to produce individual-level learning algorithms th...
This paper investigates a relatively new direction in Mul-tiagent Reinforcement Learning. Most multi...
We consider model-based multi-agent reinforcement learning, where the environment transition model i...
Due to the non-stationary environment, learning in multi-agent systems is a challenging problem. Thi...
Social (central) planning is normally used in the literature to optimize the system-wide efficiency ...
We describe a generalized Q-learning type algorithm for reinforcement learning in competitive multi-...
Melioration learning is an empirically well-grounded model of reinforcement learning. By means of co...
We study the application of multi-agent reinforcement learning for game-theoretical problems. In par...
This paper focuses on a multi-agent cooperation which is generally difficult to be achieved without ...
Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents ’ ...
This paper seeks to establish a framework for directing a society of simple, specialized, self-inter...
Machine learning and artificial intelligence has been a hot topic the last few years, thanks to impr...
Repeated play in games by simple adaptive agents is investigated. The agents use Q-learning, a speci...
Learning in the real world occurs when an agent, which perceives its current state and takes actions...
Some game theory approaches to solve multiagent reinforce-ment learning in self play, i.e. when agen...
Recent models of learning in games have attempted to produce individual-level learning algorithms th...
This paper investigates a relatively new direction in Mul-tiagent Reinforcement Learning. Most multi...
We consider model-based multi-agent reinforcement learning, where the environment transition model i...
Due to the non-stationary environment, learning in multi-agent systems is a challenging problem. Thi...
Social (central) planning is normally used in the literature to optimize the system-wide efficiency ...
We describe a generalized Q-learning type algorithm for reinforcement learning in competitive multi-...
Melioration learning is an empirically well-grounded model of reinforcement learning. By means of co...
We study the application of multi-agent reinforcement learning for game-theoretical problems. In par...
This paper focuses on a multi-agent cooperation which is generally difficult to be achieved without ...