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Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games
[article]
2020
arXiv
pre-print
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Using symmetric zero-sum matrix games, we demonstrate formally that alliance formation may be seen as a social dilemma, and empirically that na\"ive multi-agent reinforcement learning therefore fails to ...
We introduce a toy model of economic competition, and show how reinforcement learning may be augmented with a peer-to-peer contract mechanism to discover and enforce alliances. ...
Despite the fact that Matching yields a strict social dilemma while Odd One Out gives a non-strict social dilemma, the learning dynamics in both cases fail to find the optimal alliances. ...
arXiv:2003.00799v1
fatcat:evsc7hmlvndbrppcfwrnwzcvaq
Numerical analysis of a reinforcement learning model with the dynamic aspiration level in the iterated Prisoner's dilemma
2011
Journal of Theoretical Biology
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It may serve to explore the relationships between learning and evolution in social dilemma situations. ...
In the context of the iterated Prisoner's Dilemma, we numerically examine the performance of a reinforcement learning model. ...
N.M. acknowledges the support from the Grants-in-Aid for Scientific Research (No. 20760258). M.N. acknowledges the support and the Grants-in-Aid for Scientific Research from JSPS, Japan. ...
doi:10.1016/j.jtbi.2011.03.005
pmid:21397610
fatcat:vykschk6svg3ff6ynwnxljnaly
Learning dynamics in social dilemmas
2002
Proceedings of the National Academy of Sciences of the United States of America
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Using a BM stochastic learning model, we identify a fundamental solution concept for the long-term dynamics of backward-looking behavior in all social dilemmas-stochastic collusion-based on random walk ...
In social dilemmas, if aspiration levels are below maximin, then mutual or unilateral defection may also be mutually reinforcing, even though these outcomes are socially deficient. ...
doi:10.1073/pnas.092080099
pmid:12011402
pmcid:PMC128590
fatcat:rgove6gwkze65ned7ed2654yse
Improved cooperation by balancing exploration and exploitation in intertemporal social dilemma tasks
[article]
2021
arXiv
pre-print
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We demonstrate that agents that use the simple strategy improve a relatively collective return in a decision task called the intertemporal social dilemma, where the conflict between the individual and ...
We also explore the effects of the diversity of learning rates on the population of reinforcement learning agents and show that agents trained in heterogeneous populations develop particularly coordinated ...
In Figure 2 , we compare the collective return of groups performing inter-period social dilemma tasks for a fixed learning rate and dynamic learning rate. ...
arXiv:2111.09152v1
fatcat:h6s4it4yyraehmtg2ujtl5jofu
A model for the evolution of reinforcement learning in fluctuating games
2015
Animal Behaviour
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We analyse through stochastic approximation theory and simulations the learning dynamics on the behavioural timescale, and derive conditions where trial-and-error learning outcompetes hypothetical reinforcement ...
Here, we develop an evolutionary model in which individuals are genetically determined to use either trial-and-error learning or learning based on hypothetical reinforcements, and ask what is the evolutionarily ...
., 2010; Kempe & Mesoudi, 2014 ) surprisingly few studies have examined the evolution of learning for social interaction dilemmas. ...
doi:10.1016/j.anbehav.2015.01.037
fatcat:cutxjqnj6zbfnagrery2vxqsb4
Emotional Multiagent Reinforcement Learning in Social Dilemmas
[chapter]
2013
Lecture Notes in Computer Science
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Without extra mechanisms or assumptions, directly applying multiagent reinforcement learning in social dilemmas will end up with convergence to the Nash equilibrium of mutual defection among the agents ...
This paper investigates the importance of emotions in modifying agent learning behaviors in order to achieve cooperation in social dilemmas. ...
reactions affect agent learning behaviors in social dilemmas. ...
doi:10.1007/978-3-642-44927-7_25
fatcat:aqqblwmrxvh77muj4ovpxfjh6m
Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning
[article]
2019
arXiv
pre-print
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Social dilemmas have been widely studied to explain how humans are able to cooperate in society. ...
Our experiments reveal that agents trained with this dynamic learn a strategy that retaliates against defectors while promoting cooperation with other agents resulting in a prosocial society. ...
Reinforcement Learning In Multi-Agent Social Dilemmas RL is a useful tool for understanding social dilemmas. ...
arXiv:1902.03185v4
fatcat:x2mocs7ofvhbniiwm5xasmaoaa
Testing the possibility to manage cooperation in CO2 crisis through mechanisms to face the dependence of the initial condition of trust using a simulation model
2014
Revista UIS Ingenierías
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The simulation experiments offer support to our hypothesis about the possibility to manage cooperation in large-scale social dilemmas even if the trust's initial conditions are not enough to expect high ...
We tested the possibility to promote and sustain cooperation through a combination of mechanisms using a simulation model in the CO2 crisis. ...
The dynamic version of the Ostrom's mechanism of cooperation based on trust (Ostrom, 2000) for large-scale social dilemmas that we suggested worked under the conditions of this kind of social dilemmas ...
doaj:2bc663ec547b4d6c9417c58215d4c0b3
fatcat:zujvaqb73vhmpesf5k3broggvq
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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The solution of one social task often begets new social tasks, continually generating novel challenges, and thereby promoting innovation. ...
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 ...
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
Deep reinforcement learning models the emergent dynamics of human cooperation
[article]
2021
arXiv
pre-print
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spatial and temporal strategies for collective action in a social dilemma. ...
We leverage multi-agent deep reinforcement learning to model how a social-cognitive mechanism--specifically, the intrinsic motivation to achieve a good reputation--steers group behavior toward specific ...
dilemma for reinforcement learning agents. ...
arXiv:2103.04982v1
fatcat:wyj5zeflkjbo7lwqxc23cva5pi
Multi-Agent Reinforcement Learning and Human Social Factors in Climate Change Mitigation
[article]
2020
arXiv
pre-print
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We propose applying multi-agent reinforcement learning (MARL) in this setting to develop intelligent agents that can influence the social factors at play in climate change mitigation. ...
Climate change mitigation, a social dilemma made difficult by the inherent complexities of human behavior, has an impact at a global scale. ...
Multi-agent reinforcement learning is commonly demonstrated with social dilemmas (Leibo et al. 2017; Peysakhovich and Lerer 2018; Tampuu et al. 2017; Jaques et al. 2019) . ...
arXiv:2002.05147v1
fatcat:mvafm3piezheldvksszmbe7nzm
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
[article]
2017
arXiv
pre-print
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We introduce sequential social dilemmas that share the mixed incentive structure of matrix game social dilemmas but also require agents to learn policies that implement their strategic intentions. ...
In real-world social dilemmas these choices are temporally extended. Cooperativeness is a property that applies to policies, not elementary actions. ...
Acknowledgments The authors would like to thank Chrisantha Fernando, Toby Ord, and Peter Sunehag for fruitful discussions in the leadup to this work, and Charles Beattie, Denis Teplyashin, and Stig Petersen ...
arXiv:1702.03037v1
fatcat:yejw2cbprracplaw3z5pg7ghiq
Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior
[article]
2020
arXiv
pre-print
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Results suggest that considering the current situation to make decision is the worst in this kind of social dilemma game. ...
We propose to study online learning algorithm behavior in the Iterated Prisoner's Dilemma (IPD) game, where we explored the full spectrum of reinforcement learning agents: multi-armed bandits, contextual ...
in this kind of social dilemma game. ...
arXiv:2006.06580v2
fatcat:qtujt27akbayxlvtplgg5g7ixe
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas
[article]
2018
arXiv
pre-print
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Multi-agent reinforcement learning has received significant interest in recent years notably due to the advancements made in deep reinforcement learning which have allowed for the developments of new architectures ...
Using social dilemmas as the training ground, we present a novel learning architecture, Learning through Probing (LTP), where agents utilize a probing mechanism to incorporate how their opponent's behavior ...
Figure 1 : 1 Payoff Matrix for Social Dilemmas and Iterated Prisoner's Dilemma. ...
arXiv:1809.10007v2
fatcat:z7uae6syqzhvjezuh4zhg44fam
Emotional Multiagent Reinforcement Learning in Spatial Social Dilemmas
2015
IEEE Transactions on Neural Networks and Learning Systems
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This paper investigates the possibility of exploiting emotions in agent learning in order to facilitate the emergence of cooperation in social dilemmas. ...
Understanding how agents can achieve cooperation in social dilemmas through learning from local experience is a critical problem that has motivated researchers for decades. ...
[37] used a combination of replicator dynamics and switching dynamics to model multiagent learning automata in multi-state Prisoner's Dilemma (PD) games. ...
doi:10.1109/tnnls.2015.2403394
pmid:25769173
fatcat:t5tyl2hqtbbs7ogcrjj5erjkpm
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