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Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning
[article]
2021
arXiv
pre-print
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High sample complexity remains a barrier to the application of reinforcement learning (RL), particularly in multi-agent systems. ...
To address this issue, we introduce a formal notion of strategically efficient exploration in Markov games, and use this to develop two strategically efficient learning algorithms for finite Markov games ...
* This work done while at Microsoft Research Cambridge In this work, we focus on efficient exploration for reinforcement learning in competitive multi-agent settings. ...
arXiv:2107.14698v1
fatcat:lpbjs45eevcpvodibns7kthkym
A Survey of Deep Reinforcement Learning in Video Games
[article]
2019
arXiv
pre-print
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Therefore, we also discuss some key points when applying DRL methods to this field, including exploration-exploitation, sample efficiency, generalization and transfer, multi-agent learning, imperfect information ...
Deep reinforcement learning (DRL) has made great achievements since proposed. ...
Reactor [39] is a sample-efficient and numerical efficient reinforcement learning agent based on a multi-step return off-policy actor-critic architecture. ...
arXiv:1912.10944v2
fatcat:fsuzp2sjrfcgfkyclrsyzflax4
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. ...
The objective of this study is to investigate the impact of different auction pricing rules on the market performance in the context of the competitive electricity market. ...
electricity price which can indirectly enhance the competitiveness of other commodities in many other industries in the region. ...
doi:10.5539/mas.v8n1p147
fatcat:noquaae4fnhhzanktatusyhedy
Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research
[article]
2019
arXiv
pre-print
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Here we explore the hypothesis that multi-agent systems sometimes display intrinsic dynamics arising from competition and cooperation that provide a naturally emergent curriculum, which we term an autocurriculum ...
Evolution has produced a multi-scale mosaic of interacting adaptive units. ...
In addition, the first author would like to thank all the speakers, organizers, and attendees of the 2014 "Are there limits to evolution?" ...
arXiv:1903.00742v2
fatcat:xbx5ybhxkbhn7evdguxobyd2yi
Modeling the Strategic Bidding of the Producers in Competitive Electricity Markets with the Watkins' Q (lambda) Reinforcement Learning
2006
International Journal of Emerging Electric Power Systems
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The decision making process of the producers and their interactions in the market are a typical complex problem that is difficult to model explicitly, and can be studied with a multi agents approach. ...
Competition has been introduced in the last decade into the electricity markets and is presently underway in many countries. ...
Compared with the single agent learning case the multi agents learning process is a complexity framework in which the "environment" is affected by all the agents' strategic behaviors. ...
doi:10.2202/1553-779x.1258
fatcat:ybilses2drfv3iww73uzyg5dpe
Evaluation of Market Rules Using a Multi-Agent System Method
2010
IEEE Transactions on Power Systems
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Index Terms-Electricity market, market design, market power mitigation, multi-agent system, Q-learning. ...
Simulation results show that in the absence of market power mitigation, generation company (GENCO) agents facilitated by Q-learning are able to exploit the market flaws and make significantly higher profits ...
Sheblé's for the contribution in the initial stage of this research. ...
doi:10.1109/tpwrs.2009.2030379
fatcat:gozr2aibjvb5valeaghw7btakq
Reinforcement Learning for Argumentation: Describing a PhD Research
2017
International Conference on Artificial Intelligence and Law
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In multi-agent systems, each agent interacts with the environment and communicates with other agents in order to achieve the designated goal. ...
Efficiency is related to whether the agent can learn within a limited or insufficient time. So the aim is to find out if the agent can learn rapidly or not. ...
dblp:conf/icail/AlahmariYK17
fatcat:axorb2fsqjcsbednd62qom5hc4
Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination
[article]
2022
arXiv
pre-print
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Reinforcement learning (RL) approaches can illuminate emergent behaviors that facilitate coordination across teams of agents as part of a multi-agent system (MAS), which can provide windows of opportunity ...
Therefore, as part of a defense strategy, friendly forces must use strategic maneuvers and disruption to gain superiority in complex multi-faceted domains such as multi-domain operations (MDO). ...
MAS in MDO. ...
arXiv:2203.09565v1
fatcat:4getezz76ve5nf32fj2izn7bem
Strategic Interaction Multi-Agent Deep Reinforcement Learning
2020
IEEE Access
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INDEX TERMS Multi-agent deep reinforcement learning, scalability, local interaction, large scale. ...
Despite the proliferation of multi-agent deep reinforcement learning (MADRL), most existing typical methods do not scale well to the dynamics of agent populations. ...
INTRODUCTION Multi-agent reinforcement learning (MARL) sophisticatedly combines the game theory, multi-agent system and reinforcement learning (RL). ...
doi:10.1109/access.2020.3005734
fatcat:4f7qfuidbzfojjrxm2clgu7wci
Page 3927 of Mathematical Reviews Vol. , Issue 2003e
[page]
2003
Mathematical Reviews
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The authors explore the strategic trade-offs that this commitment ability and the multiplicity of tasks provide. ...
Each player attempts to improve her/his average score by adjusting the frequency of the three possible responses, using reinforcement learning. ...
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning
2022
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other. ...
This paper adopts a recursive reasoning model in a centralized-training-decentralized-execution framework to help learning agents better cooperate with or compete against others. ...
Multi-Agent Actor-Critic The actor-critic framework is a common training structure in single-agent reinforcement learning. ...
doi:10.1609/aaai.v36i7.20733
fatcat:fnmnzboidzbbplpjclc3x4wsaq
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning
[article]
2021
arXiv
pre-print
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Training a multi-agent reinforcement learning (MARL) algorithm is more challenging than training a single-agent reinforcement learning algorithm, because the result of a multi-agent task strongly depends ...
For a cooperative and competitive environment, there are generally two groups of agents: cooperative-agents and competitive-agents. ...
Recently, to enhance the performance of multi-agent reinforcement learning (MARL) algorithms, which are extensions of the RL algorithms to multi-agent settings, various strategies have been proposed. ...
arXiv:2101.06890v1
fatcat:a4cpts7rgjaffjxnli3igacizy
State-of-the-Art and Open Challenges in RTS Game-AI and Starcraft
2017
International Journal of Advanced Computer Science and Applications
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In such games, each player needs to utilize resources efficiently, which includes managing different types of soldiers, units, equipment's, economic status, positions and the uncertainty during the combat ...
Finally, we conclude by emphasizing on game 'CIG & AIIDE' competitions along with open research problems and questions in the context of RTS Game-AI, where some of the problems and challenges are mostly ...
In the proposed method "Q-learning" and "SARSA" algorithms are used with generalized reward functions to train the reinforcement learning agent. ...
doi:10.14569/ijacsa.2017.081203
fatcat:3i5ebofonzeidktjbjm6ptbora
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
eSense 2.0: Modeling Multi-agent Biomimetic Predation with Multi-layered Reinforcement Learning
[chapter]
2019
Advances in Biochemical Engineering/Biotechnology
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These independent layers split the learning objectives across multiple layers, avoiding the learning-confusion common in many multi-agent systems. ...
yet simplistic reinforcement learning algorithm that employs model-based behavior across multiple learning layers. ...
This is especially true in multi-agent systems where the desired interactions take on a strategic, intelligent meaning. ...
doi:10.1007/978-3-030-12385-7_35
fatcat:iglr4bcssjhapbiirlrwusdbiu
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