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Multi-agent Reinforcement Learning in Sequential Social Dilemmas

Leibo, JZ; Zambaldi, VF; Lanctot, M; Marecki, J; Graepel, T; (2017) Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In: Larson, K and Winikoff, M and Das, S and Durfee, EH, (eds.) Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AA-MAS 2017). (pp. pp. 464-473). ACM: New York, USA. Green open access

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Abstract

Matrix games like Prisoner's Dilemma have guided research on social dilemmas for decades. However, they necessarily treat the choice to cooperate or defect as an atomic action. In real-world social dilemmas these choices are temporally extended. Cooperativeness is a property that applies to policies, not elementary actions. 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. We analyze the dynamics of policies learned by multiple self-interested independent learning agents, each using its own deep Q-network, on two Markov games we introduce here: 1. a fruit Gathering game and 2. a Wolfpack hunting game. We characterize how learned behavior in each domain changes as a function of environmental factors including resource abundance. Our experiments show how conflict can emerge from competition over shared resources and shed light on how the sequential nature of real world social dilemmas affects cooperation.

Type: Proceedings paper
Title: Multi-agent Reinforcement Learning in Sequential Social Dilemmas
Event: 16th International Conference on Autonomous Agents and Multiagent Systems (AA-MAS 2017), 8-12 May 2017, Sao Paulo, Brazil
Open access status: An open access version is available from UCL Discovery
Publisher version: https://dl.acm.org/citation.cfm?id=3091194
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Social dilemmas, cooperation, Markov games, agent-based social simulation, non-cooperative games
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/10069053
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