Reinforcement Learning for Soccer Multi-agents System

2009 ◽  
Author(s):  
Fahimeh Farahnakian ◽  
Nasser Mozayani
Keyword(s):  
Reinforcement Learning ◽  
Multi Agents

scholarly journals Ambient Lighting Controller Based on Reinforcement Learning Components of Multi-Agents

2012 ◽  
Vol 121 (5) ◽  
Author(s):  
A. A. Bielskis ◽  
E. Guseinoviene ◽  
D. Dzemydiene ◽  
D. Drungilas ◽  
G. Gricius
Keyword(s):  
Reinforcement Learning ◽  
Ambient Lighting ◽  
Multi Agents

scholarly journals Modeling the Strategic Bidding of the Producers in Competitive Electricity Markets with the Watkins' Q (lambda) Reinforcement Learning

2006 ◽  
Vol 6 (2) ◽  
Author(s):  
Yuchao Ma ◽  
Ettore F. Bompard ◽  
Roberto Napoli ◽  
Jiang Chuanwen
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Electricity Markets ◽  
Complex Problem ◽  
Test System ◽  
Market Outcomes ◽  
Strategic Bidding ◽  
Optimal Bidding ◽  
Competitive Electricity Markets ◽  
Multi Agents

Competition has been introduced in the last decade into the electricity markets and is presently underway in many countries. A centralized approach for the dispatching of the generation units has been substituted by a market approach based on the biddings submitted by the supply side and, eventually, by the demand side. Each producer is a player in the market acting to maximize its utility. The decision making process of the producers and their interactions in the market are a typical complex problem that is difficult to be modeled explicitly, and can be studied with a multi agents approach. This paper proposes a model able to capture the decision making approach of the producers in submitting strategic biddings to the market and simulate the market outcomes resulting from those interactions. The model is based on the Watkins' Q (lambda) Reinforcement Learning and takes into account the network constraints that may pose considerable limitations to the electricity markets. The model can be used to define the optimal bidding strategy for each producer and, as well, to find the market equilibrium and assessing the market performances. The model proposed is applied to a standard IEEE 14-bus test system to illustrate its effectiveness.

Simulation of football sport PID controller based on BP neural network

2020 ◽  
pp. 1-13
Author(s):  
L.V. Qiangguo
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Single Agent ◽  
Control Effect ◽  
Learning Problem ◽  
Learning Space ◽  
Multi Agent ◽  
Multi Agents

Multi-agent reinforcement learning in football simulation can be extended by single-agent reinforcement learning. However, compared with single agents, the learning space of multi-agents will increase dramatically with the increase in the number of agents, so the learning difficulty will also increase. Based on BP neural network as the model structure foundation, this research combines PID controller to control the process of model operation. In order to improve the calculation accuracy to improve the control effect, the prediction output is obtained through the prediction model instead of the actual measured value. In addition, with the football robot as the object, this research studies the multi-agent reinforcement learning problem and its application in the football robot. The content includes single-agent reinforcement learning, multi-agent system reinforcement learning, and ball hunting, role assignment, and action selection in football robot decision strategies based on this. The simulation results show that the method proposed in this paper has certain effects.

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