Towards a Multiple-Lookahead-Levels agent reinforcement-learning technique and its implementation in integrated circuits

Al-Dayaa, Hani; Megherbi, Dalila B.

doi:10.1007/s11227-011-0738-6

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…This scheme can attempt to determine a policy and learn a maximizing cumulative reward for a faster optimal path [15,16]. RL is typically used in multi-agent-based monitoring systems to solve the problem of learning strategies using an autonomous agent [7, 17,18]. It has emerged as an area of memory capacity and computational power since the start of the use of learning algorithms [19] in multi-agent systems.…”

Section: Introductionmentioning

confidence: 99%

Autonomous and Asynchronous Triggered Agent Exploratory Path-planning Via a Terrain Clutter-index using Reinforcement Learning

Kim

2022

JICCE

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An intelligent distributed multi-agent system (IDMS) using reinforcement learning (RL) is a challenging and intricate problem in which single or multiple agent(s) aim to achieve their specific goals (sub-goal and final goal), where they move their states in a complex and cluttered environment. The environment provided by the IDMS provides a cumulative optimal reward for each action based on the policy of the learning process. Most actions involve interacting with a given IDMS environment; therefore, it can provide the following elements: a starting agent state, multiple obstacles, agent goals, and a cluttered index. The reward in the environment is also reflected by RL-based agents, in which agents can move randomly or intelligently to reach their respective goals, to improve the agent learning performance. We extend different cases of intelligent multi-agent systems from our previous works: (a) a proposed environment-clutter-based-index for agent sub-goal selection and analysis of its effect, and (b) a newly proposed RL reward scheme based on the environmental clutter-index to identify and analyze the prerequisites and conditions for improving the overall system.

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Section: Introductionmentioning

confidence: 99%

Autonomous and Asynchronous Triggered Agent Exploratory Path-planning Via a Terrain Clutter-index using Reinforcement Learning

Kim

2022

JICCE

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A hybrid P2P and master-slave cooperative distributed multi-agent reinforcement learning technique with asynchronously triggered exploratory trials and clutter-index-based selected sub-goals

Megherbi¹,

Kim²

2016

2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applicati

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A collaborative distributed multi-agent reinforcement learning technique for dynamic agent shortest path planning via selected sub-goals in complex cluttered environments

Megherbi

Kim

2015

2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision

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Towards a Multiple-Lookahead-Levels agent reinforcement-learning technique and its implementation in integrated circuits

Cited by 4 publications

References 11 publications

Autonomous and Asynchronous Triggered Agent Exploratory Path-planning Via a Terrain Clutter-index using Reinforcement Learning

Autonomous and Asynchronous Triggered Agent Exploratory Path-planning Via a Terrain Clutter-index using Reinforcement Learning

A hybrid P2P and master-slave cooperative distributed multi-agent reinforcement learning technique with asynchronously triggered exploratory trials and clutter-index-based selected sub-goals

A collaborative distributed multi-agent reinforcement learning technique for dynamic agent shortest path planning via selected sub-goals in complex cluttered environments

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