Optimization for Reinforcement Learning: From a single agent to cooperative agents

Lee Donghwan; He Niao; Kamalaruban Parameswaran; Cevher Volkan

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Optimization for Reinforcement Learning: From a single agent to cooperative agents

机译：钢筋学习优化：从单一代理到合作社

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Fueled by recent advances in deep neural networks, reinforcement learning (RL) has been in the limelight because of many recent breakthroughs in artificial intelligence, including defeating humans in games (e.g., chess, Go, StarCraft), self-driving cars, smart-home automation, and service robots, among many others. Despite these remarkable achievements, many basic tasks can still elude a single RL agent. Examples abound, from multiplayer games, multirobots, cellular-antenna tilt control, traffic-control systems, and smart power grids to network management.

机译：由于深神经网络的最近进步，加固学习（RL）已经在敏捷中，因为近期人工智能最近突破，包括在游戏中击败人类（例如，国际象棋，去，星际争霸），自驾驶汽车，聪明 - 家庭自动化和服务机器人在许多方面。尽管取得了显着的成就，但许多基本任务仍然可以避开单一的RL代理。示例比比皆是，从多人游戏，多机罗管，蜂窝天线倾斜控制，交通控制系统和智能电网到网络管理。

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《IEEE Signal Processing Magazine》 |2020年第3期|123-135|共13页
作者
Lee Donghwan; He Niao; Kamalaruban Parameswaran; Cevher Volkan;
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Univ Illinois Coordinated Sci Lab Champaign IL USA|Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea;

Univ Illinois Coordinated Sci Lab Champaign IL USA|Univ Illinois Dept Ind & Enterprise Syst Engn Champaign IL USA;

Ecole Polytech Fed Lausanne Lausanne Switzerland;

Swiss Fed Inst Technol Lausanne Lausanne Switzerland|Rice Univ Dept Elect & Comp Engn POB 1892 Houston TX 77251 USA;

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1. Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA) [J] . Deepak Annasaheb Vidhate, Parag Arun Kulkarni International Journal of Intelligent Information Systems . 2017,第6期

机译：专家代理（MCRLEA）的多代理协作强化学习
2. Cooperative Multi-Agent Reinforcement Learning-Based Co-optimization of Cores, Caches, and On-chip Network [J] . Jain Rahul, Panda Preeti Ranjan, Subramoney Sreenivas ACM Transactions on Architecture and Code Optimization . 2017,第4期

机译：基于合作多功能强化学习的核心，高速缓存和片上网络的协同优化
3. Coordinated behavior of cooperative agents using deep reinforcement learning [J] . Diallo Elhadji Amadou Oury, Sugiyama Ayumi, Sugawara Toshiharu Neurocomputing . 2020,第Jul5期

机译：利用深增强学习合作代理的协调行为
4. Self-Inference Of Others’ Policies For Homogeneous Agents In Cooperative Multi-Agent Reinforcement Learning [C] . Qifeng Lin, Qing Ling IEEE International Conference on Acoustics, Speech and Signal Processing . 2021

机译：合作多智能经纪人加固学习中众所周知的均质代理商的自我推断
5. Large-scale dynamic optimization using teams of reinforcement learning agents [D] . Crites, Robert Harry 1996

机译：使用强化学习代理团队进行大规模动态优化
6. Co-Evolution of Predator-Prey Ecosystems by Reinforcement Learning Agents [O] . Jeongho Park, Juwon Lee, Taehwan Kim, 2021

机译：加固学习代理捕食者 - 猎物生态系统的共同演变
7. Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA) [O] . Deepak Annasaheb Vidhate 2017

机译：专家代理商的多层合作加固学习（MCRLEA）

Optimization for Reinforcement Learning: From a single agent to cooperative agents

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