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Measuring the contribution of individual agents is challenging in cooperative multi-agent reinforcement learning (MARL).
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach
Brittain, M.; and Wei, P. 2019 · 1905
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Reasoning about Hypothetical Agent Behaviours and their Parameters
Albrecht, S. V.; and Stone, P. 2019 · 1906
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Behaviour suite for reinforcement learning
Osband, I.; Doron, Y.; Hessel, M.; Aslanides, J.; Sezener, E.; Saraiva, A.; McKinney, K.; Lattimore, T.; Szepesvari, C.; Singh, S.; et al. 2019 · 1908
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Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
Hu, H.; and Foerster, J. N. 2021 · 1912
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Asynchronous methods for deep reinforcement learning
Mnih, V.; Badia, A. P.; Mirza, M.; Graves, A.; Lillicrap, T.; Harley, T.; Silver, D.; and Kavukcuoglu, K. 2016a · 1937
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Asynchronous Methods for Deep Reinforcement Learning
Mnih, V.; Badia, A. P.; Mirza, M.; Graves, A.; Lillicrap, T.; Harley, T.; Silver, D.; and Kavukcuoglu, K. 2016b · 1937
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Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
Foerster, J.; Song, F.; Hughes, E.; Burch, N.; Dunning, I.; Whiteson, S.; Botvinick, M.; and Bowling, M. 2019 · 1951
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Stochastic Games*
Shapley, L. S. 1953 · 1953
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Multi-agent reinforcement learning: Independent vs. cooperative agents
Tan, M. 1993 · 1993
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Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents
Tan, M. 1997 · 1997
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Optimal payoff functions for members of collectives
Wolpert, D. H.; and Tumer, K. 2001 · 2001
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Qatten: A general framework for cooperative multiagent reinforcement learning
Yang, Y.; Hao, J.; Liao, B.; Shao, K.; Chen, G.; Liu, W.; and Tang, H. 2020b · 2002
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All learning is local: Multi-agent learning in global reward games
Chang, Y.-H.; Ho, T.; and Kaelbling, L. 2003 · 2003
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Unifying temporal and structural credit assignment problems
Agogino, A. K.; and Tumer, K. 2004 · 2004
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Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
Papoudakis, G.; Christianos, F.; Schäfer, L.; and Albrecht, S. V. 2021 · 2006
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Analyzing and visualizing multiagent rewards in dynamic and stochastic domains
Agogino, A. K.; and Tumer, K. 2008 · 2008
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Playing atari with deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Graves, A.; Antonoglou, I.; Wierstra, D.; and Riedmiller, M. 2013 · 2013
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Potential-based difference rewards for multiagent reinforcement learning
Devlin, S.; Yliniemi, L.; Kudenko, D.; and Tumer, K. 2014 · 2014
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Albrecht, S. V.; and Ramamoorthy, S. 2015 · 2015
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Emergence of Locomotion Behaviours in Rich Environments
Heess, N. M. O.; Dhruva, T.; Sriram, S.; Lemmon, J.; Merel, J.; Wayne, G.; Tassa, Y.; Erez, T.; Wang, Z.; Eslami, S. M. A.; Riedmiller, M. A.; and Silver, D. 2017 · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Lowe, R.; Wu, Y. I.; Tamar, A.; Harb, J.; Pieter Abbeel, O.; and Mordatch, I. 2017 · 2017
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Proximal Policy Optimization Algorithms
Schulman, J.; Wolski, F.; Dhariwal, P.; Radford, A.; and Klimov, O. 2017 · 2017
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Cooperative multi-agent reinforcement learning models (CMRLM) for intelligent traffic control
Vidhate, D. A.; and Kulkarni, P. 2017 · 2017
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Gep-pg: Decoupling exploration and exploitation in deep reinforcement learning algorithms
Colas, C.; Sigaud, O.; and Oudeyer, P.-Y. 2018 · 2018
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Counterfactual multi-agent policy gradients
Foerster, J.; Farquhar, G.; Afouras, T.; Nardelli, N.; and Whiteson, S. 2018 · 2018
Cited alongside, same era.
Henderson, P.; Romoff, J.; and Pineau, J. 2018 · 2018
Cited alongside, same era.
A survey on interpretable reinforcement learning
Glanois, C.; Weng, P.; Zimmer, M.; Li, D.; Yang, T.; Hao, J.; and Liu, W. 2021 · 2021
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Deep Multiagent Reinforcement-Learning-Based Resource Allocation for Internet of Controllable Things
Gu, B.; Zhang, X.; Lin, Z.; and Alazab, M. 2021 · 2021
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Explainability in deep reinforcement learning
Heuillet, A.; Couthouis, F.; and Díaz-Rodríguez, N. 2021 · 2021
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ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives
Kitamura, T.; and Yonetani, R. 2021 · 2021
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Multi-Agent Deep Reinforcement Learning for Trajectory Design and Power Allocation in Multi-UAV Networks
Zhao, N.; Liu, Z.; and Cheng, Y. 2020 · 2021
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Explainable reinforcement learning via reward decomposition
Juozapaitis, Z.; Koul, A.; Fern, A.; Erwig, M.; and Doshi-Velez, F. 2019 · 2019
Cited alongside, same era.
AI for Explaining Decisions in Multi-Agent Environments
Kraus, S.; Azaria, A.; Fiosina, J.; Greve, M.; Hazon, N.; Kolbe, L. M.; Lembcke, T.-B.; Müller, J.; Schleibaum, S.; and Vollrath, M. 2019 · 2019
Cited alongside, same era.
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks
Nasir, Y. S.; and Guo, D. 2019 · 2019
Cited alongside, same era.
The StarCraft Multi-Agent Challenge
Samvelyan, M.; Rashid, T.; de Witt, C. S.; Farquhar, G.; Nardelli, N.; Rudner, T. G. J.; Hung, C.-M.; Torr, P. H. S.; Foerster, J.; and Whiteson, S. 2019 · 2019
Cited alongside, same era.
Collaborative multiagent reinforcement learning schemes for air traffic management
Spatharis, C.; Blekas, K.; Bastas, A.; Kravaris, T.; and Vouros, G. A. 2019 · 2019
Cited alongside, same era.
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Vinyals, O.; Babuschkin, I.; Czarnecki, W. M.; Mathieu, M.; Dudzik, A.; Chung, J.; Choi, D. H.; Powell, R.; Ewalds, T.; Georgiev, P.; Oh, J.; Horgan, D.; Kroiss, M.; Danihelka, I.; Huang, A.; Sifre, L.; Cai, T.; Agapiou, J. P.; Jaderberg, M.; Vezhnevets, A. S.; Leblond, R.; Pohlen, T.; Dalibard, V.; Budden, D.; Sulsky, Y.; Molloy, J.; Paine, T. L.; Gulcehre, C.; Wang, Z.; Pfaff, T.; Wu, Y.; Ring, R.; Yogatama, D.; Wünsch, D.; McKinney, K.; Smith, O.; Schaul, T.; Lillicrap, T. P.; Kavukcuoglu, K.; Hassabis, D.; Apps, C.; and Silver, D. 2019 · 2019
Cited alongside, same era.
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Arrieta, A. B.; Díaz-Rodríguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; García, S.; Gil-López, S.; Molina, D.; Benjamins, R.; et al. 2020 · 2020
Cited alongside, same era.
Agarwal, R.; Schwarzer, M.; Castro, P. S.; Courville, A.; and Bellemare, M. G. 2022 · 2022
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Human-level play in the game of Diplomacy by combining language models with strategic reasoning
Bakhtin, A.; Brown, N.; Dinan, E.; Farina, G.; Flaherty, C.; Fried, D.; Goff, A.; Gray, J.; Hu, H.; Jacob, A. P.; Komeili, M.; Konath, K.; Kwon, M.; Lerer, A.; Lewis, M.; Miller, A. H.; Mitts, S.; Renduchintala, A.; Roller, S.; Rowe, D.; Shi, W.; Spisak, J.; Wei, A.; Wu, D. J.; Zhang, H.; and Zijlstra, M. 2022 · 2022
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Toward Policy Explanations for Multi-Agent Reinforcement Learning
Boggess, K.; Kraus, S.; and Feng, L. 2022 · 2022
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Towards a Standardised Performance Evaluation Protocol for Cooperative MARL
Gorsane, R.; Mahjoub, O.; de Kock, R.; Dubb, R.; Singh, S.; and Pretorius, A. 2022 · 2022
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Stable and efficient Shapley value-based reward reallocation for multi-agent reinforcement learning of autonomous vehicles
Han, S.; Wang, H.; Su, S.; Shi, Y.; and Miao, F. 2022 · 2022
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Collective explainable AI: Explaining cooperative strategies and agent contribution in multiagent reinforcement learning with shapley values
Heuillet, A.; Couthouis, F.; and Díaz-Rodríguez, N. 2022 · 2022
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gymnax: A JAX-based Reinforcement Learning Environment Library
Lange, R. T. 2022 · 2022
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Explainable deep reinforcement learning: state of the art and challenges
Vouros, G. A. 2022 · 2022
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SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning
Wang, J.; Zhang, Y.; Gu, Y.; and Kim, T.-K. 2022 · 2022
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Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
Wen, M.; Kuba, J. G.; Lin, R.; Zhang, W.; Wen, Y.; Wang, J.; and Yang, Y. 2022 · 2022
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The surprising effectiveness of ppo in cooperative multi-agent games
Yu, C.; Velu, A.; Vinitsky, E.; Gao, J.; Wang, Y.; Bayen, A.; and Wu, Y. 2022 · 2022
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Jumanji: a Suite of Diverse and Challenging Reinforcement Learning Environments in JAX
Bonnet, C.; Luo, D.; Byrne, D.; Abramowitz, S.; Coyette, V.; Duckworth, P.; Furelos-Blanco, D.; Grinsztajn, N.; Kalloniatis, T.; Le, V.; Mahjoub, O.; Midgley, L.; Surana, S.; Waters, C.; and Laterre, A. 2023 · 2023
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Explainable reinforcement learning for broad-xai: a conceptual framework and survey
Dazeley, R.; Vamplew, P.; and Cruz, F. 2023 · 2023
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SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning
Michalski, A.; Christianos, F.; and Albrecht, S. V. 2023 · 2023
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The Challenge of Redundancy on Multi-agent Value Factorisation
Singh, S. S.; and Rosman, B. 2023 · 2023
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