A multiagent reinforcement learning method based on the model inference of the other agents

2002 ◽  
Vol 33 (12) ◽  
pp. 67-76
Author(s):  
Yoichiro Matsuno ◽  
Tatsuya Yamazaki ◽  
Jun Matsuda ◽  
Shin Ishii
Keyword(s):  
Reinforcement Learning ◽  
The Other ◽  
Learning Method ◽  
Model Inference ◽  
Multiagent Reinforcement Learning

scholarly journals SA-IGA: a multiagent reinforcement learning method towards socially optimal outcomes

2019 ◽  
Vol 33 (4) ◽  
pp. 403-429 ◽  
Author(s):  
Chengwei Zhang ◽  
Xiaohong Li ◽  
Jianye Hao ◽  
Siqi Chen ◽  
Karl Tuyls ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Method ◽  
Optimal Outcomes ◽  
Multiagent Reinforcement Learning

scholarly journals S$$^{2}$$ES: a stationary and scalable knowledge transfer approach for multiagent reinforcement learning

2021 ◽  
Author(s):  
Tonghao Wang ◽  
Xingguang Peng ◽  
Demin Xu
Keyword(s):  
Reinforcement Learning ◽  
Knowledge Transfer ◽  
Shannon Entropy ◽  
Empirical Studies ◽  
The Other ◽  
Learning Pattern ◽  
Learning Speed ◽  
Multiagent Reinforcement Learning ◽  
Experience Replay ◽  
Data Efficiency

AbstractKnowledge transfer is widely adopted in accelerating multiagent reinforcement learning (MARL). To accelerate the learning speed of MARL for learning-from scratch agents, in this paper, we propose a Stationary and Scalable knowledge transfer approach based on Experience Sharing (S$$^{2}$$ 2 ES). The mainframe of our approach is structured into three components: what kind of experience, how to learn, and when to transfer. Specifically, we first design an augmented form of experience. By sharing (i.e., transmitting) the experience from one agent to its peers, the learning speed can be effectively enhanced with guaranteed scalability. A synchronized learning pattern is then adopted, which reduces the nonstationarity brought by experience replay, and at the same time retains data efficiency. Moreover, to avoid redundant transfer when the agents’ policies have converged, we further design two trigger conditions, one is modified Q value-based and another is normalized Shannon entropy-based, to determine when to conduct experience sharing. Empirical studies indicate that the proposed approach outperforms the other knowledge transfer methods in efficacy, efficiency, and scalability. We also provide ablation experiments to demonstrate the necessity of the key ingredients.

A Plant Control Technology Using Reinforcement Learning Method with Automatic Reward Adjustment

2009 ◽  
Vol 129 (7) ◽  
pp. 1253-1263
Author(s):  
Toru Eguchi ◽  
Takaaki Sekiai ◽  
Akihiro Yamada ◽  
Satoru Shimizu ◽  
Masayuki Fukai
Keyword(s):  
Reinforcement Learning ◽  
Control Technology ◽  
Learning Method ◽  
Plant Control

Control of an Inverted Pendulum by Reinforcement Learning Method in PLC Environment

2020 ◽  
Author(s):  
Gokhan Demirkiran ◽  
Ozcan Erdener ◽  
Onay Akpinar ◽  
Pelin Demirtas ◽  
M. Yagiz Arik ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Inverted Pendulum ◽  
Learning Method
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