Multi-agent reinforcement learningCombinatorial optimization problemsMultiple traveling salesman problemsGraph neural networksPolicy networksThis paper proposes a learning-based approach to optimize the multiple traveling salesman problem (MTSP), which is one classic representative of cooperative combinatorial ...
I understand you are looking for a way to simulate a multi-agent Reinforcement Learning environment. Unfortunately, the Reinforcement Learning Toolbox currently does not support multi-agent scenario. You would need to write your custom environment and training algorithms for such scenario. ...
Learning from Multiple Independent Advisors in Multi-agentReinforcement LearningSriram Ganapathi SubramanianVector Institute, Toronto, CanadaUniversity of Waterloo, Waterloo, Canadasriram.subramanian@vectorinstitute.aiMatthew E. TaylorUniversity of Alberta, Edmonton, CanadaAlberta Machine Intelligence Institute, Edmo...
第一个GPT代理的损失函数设计如下: L1(x;Θ1)=[logP(x)Prior−logP(x)Agent1+σ1⋅s(x)]2 其中Θ1是第一个代理的参数,x是生成的分子,σ1是控制分数项的系数,P(x)_{model}指的是模型生成x的可能性。应该注意的是,通常P(x)_{\text{Prior}} < P(x)_{\text{Agent}}。 此外,我们...
A Task Offloading and Resource Allocation Strategy Based on Multi-Agent Reinforcement Learning in Mobile Edge Computing 2024, Future Internet AMTOS: An ADMM-Based Multilayer Computation Offloading and Resource Allocation Optimization Scheme in IoV-MEC System 2024, IEEE Internet of Things Journal Optimizing...
Whittle index is a heuristic tool that leads to good performance for the restless bandits problem. In this paper, we extend Whittle index to a new multi-agent reinforcement learning (MARL) setting with multiple discrete actions and a possibly changing constraint on the state space, resulting in ...
Last but not least, we believe that the feedback recovery mechanism and the two-stage action selection mechanism can also be used in general distributed multi-agent reinforcement learning problems in which feedback information on rewards can be corrupted. 展开 ...
Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience. Appl. Energy 336, 120826. https://doi.org/10.1016/j.apenergy.2023.120826 (2023). Article Google Scholar Liu, H., Li, J. & Ge, S. Research on hierarchical control and...
Diepold, Multi-agent deep reinforcement learning: a survey. Artif. Intell. Rev. 55, 895–943 (2022). https://doi.org/10.1007/s10462-021-09996-w Article Google Scholar M.Z. Gunduz, R. Das, Cyber-security on smart grid: threats and potential solutions. Comput. Netw. 169, 107094 (...
For this example you create two reinforcement learning agents. Both agents operate at the same sample time in this example. Set the sample time value (in seconds). Get Ts = 0.1; When you create the agent, the initial parameters of the critic network are initialized with random values. Fix...