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Using Multi-Agent Reinforcement Learning in Auction Simulations
[article]
2020
arXiv
pre-print
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The results show that using a multi-agent reinforcement learning strategy improves the outcomes of the auction simulations. ...
In this study, the strategic (rational) agents created by reinforcement learning algorithms are supposed to be bidder agents in various types of auction mechanisms such as British Auction, Sealed Bid Auction ...
In this paper, multi-agent reinforcement learning agents in different auction setups were created. ...
arXiv:2004.02764v1
fatcat:haxdupddxbg4dazwrv5qbq4xxe
Learning Task Performance in Market-Based Task Allocation
[chapter]
2013
Advances in Intelligent Systems and Computing
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Experimental results demonstrate that agents that model team member performance using this approach can more efficiently distribute tasks in multi-agent auctions. ...
A policy gradient reinforcement learning algorithm is used to learn a cost factor that can be applied individually to auction bids. ...
In Section II, we present the background and related work for learning in multi-agent auctions. In Section III, we discuss the use of the reinforcement learning algorithm within an auction framework. ...
doi:10.1007/978-3-642-33932-5_57
fatcat:rxvt75ge6bagranghcysnbllki
Designing Multi-unit Multiple Bid Auctions: An Agent-based Computational Model of Uniform, Discriminatory and Generalised Vickrey Auctions
2007
The Economic Record
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Designing multi-unit multiple bid auctions: an agent-based computational model of uniform, discriminatory and generalised Vickrey Auctions. Economic Record, 83 (S1), S57-S72. ...
An agent-based model was then formulated to simulate bidding among a population of agents that use a reinforcement learning algorithm to update their bids based on individual experience. ...
In the third section, we develop an agent-based model (ABM) of boundedly rational bidders revising their bid choices using Erev and Roth's (1998) reinforcement learning algorithm. ...
doi:10.1111/j.1475-4932.2007.00410.x
fatcat:hjnmvgaqorb5hhn5ci5mlifa6a
Market-Based Dynamic Task Allocation Using Heuristically Accelerated Reinforcement Learning
[chapter]
2011
Lecture Notes in Computer Science
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, and use Heuristically Accelerated Reinforcement Learning to evaluate their aptitude to perform these roles, given the situation of the team, in real-time. ...
This paper presents a Multi-Robot Task Allocation (MRTA) system, implemented on a RoboCup Small Size League team, where robots participate of auctions for the available roles, such as attacker or defender ...
The authors also believe that more research into the topic of transfer learning for RL domains [19] could considerably improve the capabilities to apply what was learned in simulation to real robots. ...
doi:10.1007/978-3-642-24769-9_27
fatcat:joi2w7jnvvf3tbqqtyvbz3zzp4
Comparison of Different Auction Pricing Rules in the Electricity Market
2014
Modern Applied Science
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The Simulated-Annealing Q-learning algorithm has been adopted as the learning mechanism for the agents so they can maximize their profit using strategic bidding. ...
Using agent-based modeling approach, generators have been modelled as agents submitting price-quantity bids to the market. ...
market to be designed carefully to ensure that (1) it is attractive for new players, which hopefully will bring about newer and cleaner power generation technology into use, and (2) it results in a competitive ...
doi:10.5539/mas.v8n1p147
fatcat:noquaae4fnhhzanktatusyhedy
Deep Reinforcement Learning for Sponsored Search Real-time Bidding
[article]
2018
arXiv
pre-print
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We also extend the method to handle the multi-agent problem. We deployed the SS-RTB system in the e-commerce search auction platform of Alibaba. ...
We propose a reinforcement learning (RL) solution for handling the complex dynamic environment. ...
No previous work has explored using cooperative rewards to address the multi-agent problem in such scenarios. ...
arXiv:1803.00259v1
fatcat:kqjd46oojfdh5p2kin42w7muua
A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning
2014
2014 International Joint Conference on Neural Networks (IJCNN)
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Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. ...
multi-agent learning domain has not yet been thoroughly investigated. ...
Multi-objective reinforcement learning (MORL) is an extension to reinforcement learning (RL) where the environment provides the agent with multiple feedback signals. ...
doi:10.1109/ijcnn.2014.6889637
dblp:conf/ijcnn/MoffaertBCELN14
fatcat:cdg6m4gzo5amxeswcg6egcahue
Evaluating Strategic Structures in Multi-Agent Inverse Reinforcement Learning
2021
The Journal of Artificial Intelligence Research
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We show how to efficiently and scalably extend inverse reinforcement learning to multi-agent settings, by reducing the multi-agent problem to N single-agent problems while still satisfying rationality ...
A core question in multi-agent systems is understanding the motivations for an agent's actions based on their behavior. ...
multi-agent inverse reinforcement learning and inverse game theory. ...
doi:10.1613/jair.1.12594
fatcat:evyejc6srnafbb3a6zvsrktf2m
Comparing Policy Gradient and Value Function Based Reinforcement Learning Methods in Simulated Electrical Power Trade
2012
IEEE Transactions on Power Systems
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Graeme (2012) Comparing policy gradient and value function based reinforcement learning methods in simulated electrical power trade. ...
Abstract-In electrical power engineering, reinforcement learning algorithms can be used to model the strategies of electricity market participants. ...
Similarly, the authors are very grateful to the researchers from Dalle Molle Institute for Artificial Intelligence (IDSIA) and the Technical University of Munich involved in developing the reinforcement ...
doi:10.1109/tpwrs.2011.2166091
fatcat:wgiev5eo3zfh3aypbx4l675osu
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising
[article]
2018
arXiv
pre-print
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Other Versions
In this paper, we formulate bidding optimization with multi-agent reinforcement learning. ...
A practical Distributed Coordinated Multi-Agent Bidding (DCMAB) has been proposed and implemented to balance the tradeoff between the competition and cooperation among advertisers. ...
In our work, we adopt multi-agent reinforcement learning to achieve such a goal. Multi-agent Reinforcement Learning. ...
arXiv:1802.09756v1
fatcat:f4ikpoio3neyxfziactsvjwdxu
Market-Driven Multi-Agent Collaboration in Robot Soccer Domain
[chapter]
2005
Cutting Edge Robotics
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Although Teambots is not used in any international robot soccer competition, it is a well-known multi-purpose simulator. ...
multi-agent teams in key application areas. ...
The chapter IV is devoted to research on adaptive and learning systems in mobile robots area. The chapter V speaks about different application areas of multi-robot systems. ...
doi:10.5772/4661
fatcat:bhi7duaoerfqhaspd27arc6yta
A Systematic Approach for Including Machine Learning in Multi-agent Systems
[chapter]
2005
Lecture Notes in Computer Science
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Large scale multi-agent systems (MASs) in unpredictable environments must use machine learning techniques to perform their goals and improve the performance of the system. ...
This paper presents a systematic approach to introduce machine learning in the design and implementation phases of a software agent. ...
Introduction Multi-Agent Systems (MASs) [1] [2] is a new technology that has been recently used in many simulators and intelligent systems to help humans perform several timeconsuming tasks. ...
doi:10.1007/11426714_14
fatcat:h5el2dxynzcsrj237gpmnasrly
On the Importance of Opponent Modeling in Auction Markets
[article]
2019
arXiv
pre-print
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To this end, we demonstrate the efficacy of applying opponent-modeling in a number of simulated market settings. ...
While our simulations are simplified representations of actual market dynamics, they provide an idealized "playground" in which our techniques can be demonstrated and tested. ...
in any way for evaluating the merits of participating in any transaction. ...
arXiv:1911.12816v1
fatcat:7io7sz4xnrex7pzzrk7lefcipy
What Format for Multi-Unit Multiple-Bid Auctions?
2010
Computational Economics
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However, there is great uncertainty about the best auction formats when multi-unit auctions are used. ...
This paper uses computational experiments where bidders learn over nonlinear bidding strategies to compare outcomes for alternative pricing format for multi-unit multiple-bid auctions. ...
In our simulations, we find a much greater variability in auction outcomes across replications when reinforcement learning used. ...
doi:10.1007/s10614-010-9199-x
fatcat:aeikd6o3ijav7pvk6kibei5zba
Comparison of Market-based and DQN methods for Multi-Robot processing Task Allocation (MRpTA)
2020
2020 Fourth IEEE International Conference on Robotic Computing (IRC)
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Market-based methods are a natural candidate to deal processing tasks over a MRS but recent and numerous developments in reinforcement learning and especially Deep Q-Networks (DQN) provide new opportunities ...
Our study shows the relevancy of learning-based methods and also highlight research challenges to solve the processing load-balancing problem in MRS. ...
To overcome this problem, we propose another approach based on reinforcement learning.
B. Reinforcement learning One of the main goals of our RL approach is to remove the auction system. ...
doi:10.1109/irc.2020.00060
fatcat:n5cv2jqcnnerrdkj673sgpe3ue
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