Modular Learning Systems for Behavior Acquisition in Multi-Agent Environment

Takahashi, Yasutake; Asada, Minoru

doi:10.5772/5283

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Modular Reinforcement Learning1

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2011

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“…The work [30] gives a full review of di erent action selection [56,68] use gating signals to decide which module is responsible in each state and some authors have studied how to share the reward among modules [77]. The main advantage of this approach is that it allows to learn di erent concurrent subtasks in a fairly simple way.…”

Section: Modular Reinforcement Learningmentioning

confidence: 99%

Cooperative Multi-Agent Reinforcement Learning for Multi-Component Robotic Systems: guidelines for future research

Graña

Fernández-Gauna

López-Guede

2011

Paladyn, Journal of Behavioral Robotics

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Reinforcement Learning (RL) as a paradigm aims to develop algorithms that allow to train an agent to optimally achieve a goal with minimal feedback information about the desired behavior, which is not precisely specified. Scalar rewards are returned to the agent as response to its actions endorsing or opposing them. RL algorithms have been successfully applied to robot control design. The extension of the RL paradigm to cope with the design of control systems for Multi-Component Robotic Systems (MCRS) poses new challenges, mainly related to coping with scaling up of complexity due to the exponential state space growth, coordination issues, and the propagation of rewards among agents. In this paper, we identify the main issues which o er opportunities to develop innovative solutions towards fully-scalable cooperative multi-agent systems.

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Section: Modular Reinforcement Learningmentioning

confidence: 99%