Introduction to Multi Agent Cooperative

In multi-agent cooperative systems, in addition to the total additive noise and time-variant delay, a multiplicative complex random variable is considered to model the main received signal and its replicas due to multipath propagation.

To give agents more suitable rewards in a multi-agent environment, a multi-agent reward-iteration fuzzy Q-learning (RIFQ) is proposed for multi-agent cooperative tasks.

Simultaneously, utilizing the hierarchical consensus module and a self-supervised intrinsic reward enhanced decision module, the proposed cooperative MARL algorithm Rochico can output the final diversified multi-agent cooperative policy.

Multi-agent cooperative target tracking plays a significant role in distributed artificial intelligence for unmanned aerial vehicle (UAV) systems.

Multi-agent cooperative SLAM is the precondition of multi-user AR interaction.

This paper proposes a method to mitigate the significant performance degradation due to planned suspensions in the multi-agent cooperative patrol problem.

This paper focuses on solving the multi-agent cooperative target search problem with the demand for obtaining the maximal cumulative detection reward, given the prior target probability map and the sensor detection ability under various constraints.

In order to verify the effectiveness of the method, we constructed a validation system based on the network structure of a multi-agent cooperative simulation system with known credibility.

In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport.

Specifically, we model the real-time scheduling process as a multi-agent cooperative game and propose multi-agent self-cooperative learning that overcomes the curse of dimensionality and credit assignment problems.

In addition, a multi-agent cooperative game benefit distribution model was constructed based on the Shapley value method, the Banzhaf value method, the solidarity value method, and the improved risk factor.

In order to realize the local consumption of surplus electric energy, this paper proposes a multi-agent cooperative game model of power grid-generator-electrolytic aluminum based on Nash negotiation theory.

In this paper, we propose a novel multi-objective cooperative bid optimization formulation called Multi-Agent Cooperative bidding Games (MACG).

The experimental results show that GraphComm can improve the effectiveness, robustness and efficiency of communication in multi-agent cooperative tasks as compared with baseline methods.