Web4 de set. de 2024 · To address this problem, we had analyzed the newest existing framework, Hierarchical Actor-Critic with Hindsight (HAC), test it in the simulated mobile robot environment and determine the optimal configuration of parameters and ways to encode information about the environment states. Keywords. Hierarchical Actor-Critic; … WebFinally, the soft actor-critic (SAC) is used to optimize agents' actions in training for compliance control. We conduct experiments on the Food Collector task and compare HRG-SAC with three baseline methods. The results demonstrate that the hierarchical relation graph can significantly improve MARL performance in the cooperative task.
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WebWe reformulate this decision process into a hierarchical reinforcement learning task and develop a novel hierarchical reinforced urban planning framework. This framework includes two components: 1) In region-level configuration, we present an actor- critic based method to overcome the challenge of weak reward feedback in planning the urban functions of … Web18 de mar. de 2024 · Afterward, a neural network-based actor-critic structure is built for approximating the iterative control policies and value functions. Finally, a large-scale formation control problem is provided to demonstrate the performance of our developed hierarchical leader-following formation control structure and MsGPI algorithm. flip up rack mount monitor
Hierarchical Multiagent Formation Control Scheme via Actor-Critic ...
Web14 de abr. de 2024 · However, these 2 settings limit the R-tree building results as Sect. 1 and Fig. 1 show. To overcome these 2 limitations and search a better R-tree structure from the larger space, we utilize Actor-Critic [], a DRL algorithm and propose ACR-tree (Actor-Critic R-tree), of which the framework is shown in Fig. 2.We use tree-MDP (M1, Sect. … Web27 de set. de 2024 · To resolve these limitations, we propose a model that conducts both representation learning for multiple agents using hierarchical graph attention network … Web17 de jun. de 2024 · We show that one can design even more data-efficient hierarchical RL algorithms by reframing the objective of HDQN at each level of abstractions, as a maximum entropy reinforcement learning (ME-RL) and utilizing soft-actor critic (SAC) method of [2]. flip up radio head unit