A study on hierarchical modular reinforcement learning for multi-agent pursuit problem based on relative coordinate states

Wada, Tatsuya; Okawa, Takuya; Watanabe, Toshihiko

doi:10.1109/cira.2009.5423188

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Moreover, because the surrounding environment is complex, the agents cannot express the collaboration. [6], [7] proposed the hierarchical modular reinforcement learning to solve the above problems. It is difficult to decide how many kinds of sub-task should be decomposed into.…”

Section: B Hierarchical Modular Reinforcement Learning Methodsmentioning

confidence: 99%

“…In order to solve these problems several types of hierarchical reinforcement learning have been proposed to apply actual applications [6], [7]. Hierarchical Modular Reinforcement Learning (HMRL), consists of 2 layered learning where Profit Sharing works to plan a prey position in the higher layer and Q-learning method trains the state-actions to the target in the c 2013 IEEE.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical modular reinforcement learning method and knowledge acquisition of state-action rule for multi-target problem

Ichimura

Igaue

2013

2013 IEEE 6th International Workshop on Computational Intelligence and Applications (IWCIA)

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Hierarchical Modular Reinforcement Learning (HMRL), consists of 2 layered learning where Profit Sharing works to plan a prey position in the higher layer and Q-learning method trains the state-actions to the target in the lower layer. In this paper, we expanded HMRL to multi-target problem to take the distance between targets to the consideration. The function, called 'AT field', can estimate the interests for an agent according to the distance between 2 agents and the advantage/disadvantage of the other agent. Moreover, the knowledge related to stateaction rules is extracted by C4.5. The action under the situation is decided by using the acquired knowledge. To verify the effectiveness of proposed method, some experimental results are reported.

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Section: B Hierarchical Modular Reinforcement Learning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical modular reinforcement learning method and knowledge acquisition of state-action rule for multi-target problem

Ichimura

Igaue

2013

2013 IEEE 6th International Workshop on Computational Intelligence and Applications (IWCIA)

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Multi-agent cooperation pursuit based on an extension of AALAADIN organisational model

Souidi

Piao

Guo

et al. 2015

Journal of Experimental & Theoretical Artificial Intelligen

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Multi-Agent Pursuit-Evasion Game Based on Organizational Architecture

Souidi

Siam

Pei

et al. 2019

CIT. J. comput. inf. technol

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Multi-agent coordination mechanisms are frequently used in pursuit-evasion games with the aim of enabling the coalitions of the pursuers and unifying their individual skills to deal with the complex tasks encountered. In this paper, we propose a coalition formation algorithm based on organizational principles and applied to the pursuit-evasion problem. In order to allow the alliances of the pursuers in different pursuit groups, we have used the concepts forming an organizational modeling framework known as YAMAM (Yet Another Multi Agent Model). Specifically, we have used the concepts Agent, Role, Task, and Skill, proposed in this model to develop a coalition formation algorithm to allow the optimal task sharing. To control the pursuers' path planning in the environment as well as their internal development during the pursuit, we have used a Reinforcement learning method (Q-learning). Computer simulations reflect the impact of the proposed techniques. ACM CCS (2012) Classification: Computing methodologies → Artificial intelligence → Distributed artificial intelligence → Multi-agent systems Theory of computation → Theory and algorithms for application domains → Algorithmic game theory and mechanism design → Convergence and learning in games

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A study on hierarchical modular reinforcement learning for multi-agent pursuit problem based on relative coordinate states

Cited by 3 publications

References 16 publications

Hierarchical modular reinforcement learning method and knowledge acquisition of state-action rule for multi-target problem

Hierarchical modular reinforcement learning method and knowledge acquisition of state-action rule for multi-target problem

Multi-agent cooperation pursuit based on an extension of AALAADIN organisational model

Multi-Agent Pursuit-Evasion Game Based on Organizational Architecture

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