scholarly journals Optimization of Stock Trading System based on Multi-Agent Q-Learning Framework

2004 ◽  
Vol 11B (2) ◽  
pp. 207-212
Keyword(s):  
Trading System ◽  
Stock Trading ◽  
Q Learning ◽  
Learning Framework ◽  
Multi Agent

scholarly journals Reinforcement Learning under Threats

2019 ◽  
Vol 33 ◽  
pp. 9939-9940 ◽  
Author(s):  
Victor Gallego ◽  
Roi Naveiro ◽  
David Rios Insua
Keyword(s):  
Reinforcement Learning ◽  
Single Agent ◽  
Potential Threat ◽  
Q Learning ◽  
Learning Framework ◽  
Opponent Modeling ◽  
Theoretical Approaches ◽  
New Learning ◽  
Markov Decision ◽  
Multi Agent

In several reinforcement learning (RL) scenarios, mainly in security settings, there may be adversaries trying to interfere with the reward generating process. However, when non-stationary environments as such are considered, Q-learning leads to suboptimal results (Busoniu, Babuska, and De Schutter 2010). Previous game-theoretical approaches to this problem have focused on modeling the whole multi-agent system as a game. Instead, we shall face the problem of prescribing decisions to a single agent (the supported decision maker, DM) against a potential threat model (the adversary). We augment the MDP to account for this threat, introducing Threatened Markov Decision Processes (TMDPs). Furthermore, we propose a level-k thinking scheme resulting in a new learning framework to deal with TMDPs. We empirically test our framework, showing the benefits of opponent modeling.

scholarly journals Boltzmann Distributed Replicator Dynamics: Population Games in a Microgrid Context

Games ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Gustavo Chica-Pedraza ◽  
Eduardo Mojica-Nava ◽  
Ernesto Cadena-Muñoz
Keyword(s):  
Control Method ◽  
Optimization Problems ◽  
Replicator Dynamics ◽  
Boltzmann Distribution ◽  
Full Information ◽  
Multi Agent Systems ◽  
Q Learning ◽  
Population Games ◽  
Distributed Approach ◽  
Multi Agent

Multi-Agent Systems (MAS) have been used to solve several optimization problems in control systems. MAS allow understanding the interactions between agents and the complexity of the system, thus generating functional models that are closer to reality. However, these approaches assume that information between agents is always available, which means the employment of a full-information model. Some tendencies have been growing in importance to tackle scenarios where information constraints are relevant issues. In this sense, game theory approaches appear as a useful technique that use a strategy concept to analyze the interactions of the agents and achieve the maximization of agent outcomes. In this paper, we propose a distributed control method of learning that allows analyzing the effect of the exploration concept in MAS. The dynamics obtained use Q-learning from reinforcement learning as a way to include the concept of exploration into the classic exploration-less Replicator Dynamics equation. Then, the Boltzmann distribution is used to introduce the Boltzmann-Based Distributed Replicator Dynamics as a tool for controlling agents behaviors. This distributed approach can be used in several engineering applications, where communications constraints between agents are considered. The behavior of the proposed method is analyzed using a smart grid application for validation purposes. Results show that despite the lack of full information of the system, by controlling some parameters of the method, it has similar behavior to the traditional centralized approaches.

scholarly journals Deep Reinforcement Learning based Multi-Agent Collaborated Network for Distributed Stock Trading

2018 ◽  
Vol 11 (2) ◽  
pp. 11-20 ◽  
Author(s):  
Jung-Jae Kim ◽  
Si-Ho Cha ◽  
Kuk-Hyun Cho ◽  
Minwoo Ryu
Keyword(s):  
Reinforcement Learning ◽  
Stock Trading ◽  
Multi Agent

Modular Production Control with Multi-Agent Deep Q-Learning

2021 ◽  
Author(s):  
Dennis Gankin ◽  
Sebastian Mayer ◽  
Jonas Zinn ◽  
Birgit Vogel-Heuser ◽  
Christian Endisch
Keyword(s):  
Production Control ◽  
Q Learning ◽  
Modular Production ◽  
Multi Agent

Improvement on Supporting Machine Learning Algorithm for Solving Problem in Immediate Decision Making

2012 ◽  
Vol 566 ◽  
pp. 572-579
Author(s):  
Abdolkarim Niazi ◽  
Norizah Redzuan ◽  
Raja Ishak Raja Hamzah ◽  
Sara Esfandiari
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Multi Agent Systems ◽  
Combined Model ◽  
Q Learning ◽  
Agent Systems ◽  
Multi Agent ◽  
Case Base ◽  
Case Base Reasoning ◽  
Robotic Tool

In this paper, a new algorithm based on case base reasoning and reinforcement learning (RL) is proposed to increase the convergence rate of the reinforcement learning algorithms. RL algorithms are very useful for solving wide variety decision problems when their models are not available and they must make decision correctly in every state of system, such as multi agent systems, artificial control systems, robotic, tool condition monitoring and etc. In the propose method, we investigate how making improved action selection in reinforcement learning (RL) algorithm. In the proposed method, the new combined model using case base reasoning systems and a new optimized function is proposed to select the action, which led to an increase in algorithms based on Q-learning. The algorithm mentioned was used for solving the problem of cooperative Markov’s games as one of the models of Markov based multi-agent systems. The results of experiments Indicated that the proposed algorithms perform better than the existing algorithms in terms of speed and accuracy of reaching the optimal policy.

Sign in / Sign up

Export Citation Format

Share Document