Multi-Agent Cooperative Fuzzy Reinforcement Learning
RM Haighton - 2023 - repository.library.carleton.ca
This thesis explores multi-agent cooperative reinforcement learning using fuzzy systems.
Two main problems are studied: multi-agent systems learning altruism, and cooperative …
Two main problems are studied: multi-agent systems learning altruism, and cooperative …
An Adaptable Fuzzy Reinforcement Learning Method for Non-Stationary Environments
How do we know when a reinforcement learning policy needs to adapt? In nonstationary
environments, agents must adapt and learn in environments that change dynamically. We …
environments, agents must adapt and learn in environments that change dynamically. We …
Advantages of cooperation between reinforcement learning agents in difficult stochastic problems
HR Berenji, D Vengerov - … on Fuzzy Systems. FUZZ-IEEE 2000 …, 2000 - ieeexplore.ieee.org
Presents the first results in understanding the reasons for cooperative advantage between
reinforcement learning agents. We consider a cooperation method which consists of using …
reinforcement learning agents. We consider a cooperation method which consists of using …
Learning to communicate in cooperative multi-agent reinforcement learning
E Pesce - 2023 - wrap.warwick.ac.uk
Recent advances in deep reinforcement learning have produced unprecedented results.
The success obtained on single-agent applications led to exploring these techniques in the …
The success obtained on single-agent applications led to exploring these techniques in the …
Evolutionary learning, reinforcement learning, and fuzzy rules for knowledge acquisition in agent-based systems
A Bonarini - Proceedings of the IEEE, 2001 - ieeexplore.ieee.org
The behavior of agents in complex and dynamic environments cannot be programmed a
priori, but needs to self-adapt to the specific situations. We present some approaches based …
priori, but needs to self-adapt to the specific situations. We present some approaches based …
A hybrid multiagent reinforcement learning approach using strategies and fusion
Reinforcement Learning comprises an attractive solution to the problem of coordinating a
group of agents in a Multiagent System, due to its robustness for learning in uncertain and …
group of agents in a Multiagent System, due to its robustness for learning in uncertain and …
Social interaction of cooperative communication and group generation in multi-agent reinforcement learning systems
K Zhang, Y Maeda, Y Takahashi - 2011 IEEE International …, 2011 - ieeexplore.ieee.org
Recently, researches on multi-agent systems (MAS) which autonomous agents are able to
learn cooperative behavior are actively performed. It is necessary for social agents to …
learn cooperative behavior are actively performed. It is necessary for social agents to …
Reinforcement learning with multiple shared rewards
DM Guisi, R Ribeiro, M Teixeira, AP Borges… - Procedia Computer …, 2016 - Elsevier
A major concern in multi-agent coordination is how to select algorithms that can lead agents
to learn together to achieve certain goals. Much of the research on multi-agent learning …
to learn together to achieve certain goals. Much of the research on multi-agent learning …
Learning and stabilization of altruistic behaviors in multi-agent systems by reciprocity
Optimization of performance in collective systems often requires altruism. The emergence
and stabilization of altruistic behaviors are difficult to achieve because the agents incur a …
and stabilization of altruistic behaviors are difficult to achieve because the agents incur a …
[PDF][PDF] Temporal difference and return optimism in cooperative multi-agent reinforcement learning
A key aim of artificial intelligence is to build agents that can cooperate or compete with one
another effectively while acting independently, in spite of incomplete information and a …
another effectively while acting independently, in spite of incomplete information and a …