Cooperative Multi-Agent Reinforcement Learning for Multi-Component Robotic Systems: guidelines for future research
نویسندگان
چکیده
Reinforcement Learning (RL) 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 succesfully 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, maily related to coping with scaling complexity up 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 offer oportunities to develop innovative solutions towards fully-scalable cooperative multi-agent systems.
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عنوان ژورنال:
- Paladyn
دوره 2 شماره
صفحات -
تاریخ انتشار 2011