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Intuitively, experience playing against one mixture of opponents in a given domain should be relevant for a different mixture in the same domain.
Adaptive mixtures of local experts
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McMahan, H. B., Gordon, G. J., and Blum, A · 2003
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Wang, X. and Sandholm, T · 2003
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Jong, N. K. and Stone, P · 2005
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Konidaris, G. and Barto, A · 2006
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Walsh, T. J., Li, L., and Littman, M. L · 2006
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Computing robust counter-strategies
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Pan, S. J. and Yang, Q · 2010
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Online implicit agent modelling
Bard, N., Johanson, M., Burch, N., and Bowling, M · 2013
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2014
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Learning potential functions and their representations for multi-task reinforcement learning
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Autonomous cross-domain knowledge transfer in lifelong policy gradient reinforcement learning
Ammar, H. B., Eaton, E., Luna, J. M., and Ruvolo, P · 2015
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Deep decentralized multi-task multi-agent reinforcement learning under partial observability
Omidshafiei, S., Pazis, J., Amato, C., How, J. P., and Vian, J · 2017
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A multi-agent reinforcement learning model of common-pool resource appropriation
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An overview of multi-task learning in deep neural networks
Ruder, S · 2017
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Mix & match agent curricula for reinforcement learning
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QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning
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Cooperating with unknown teammates in complex domains: A robot soccer case study of ad hoc teamwork
Barrett, S. and Stone, P · 2015
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Policy distillation
Rusu, A. A., Colmenarejo, S. G., Gulcehre, C., Desjardins, G., Kirkpatrick, J., Pascanu, R., Mnih, V., Kavukcuoglu, K., and Hadsell, R · 2015
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Opponent modeling in deep reinforcement learning
He, H., Boyd-Graber, J., Kwok, K., and III, H. D · 2016
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Multi-agent reinforcement learning as a rehearsal for decentralized planning
Kraemer, L. and Banerjee, B · 2016
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Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R · 2016
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Deep reinforcement learning with double Q-learning
van Hasselt, H., Guez, A., and Silver, D · 2016
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Progress & compress: A scalable framework for contiual learning
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Value-decomposition networks for cooperative multi-agent learning
Sunehag, P., Lever, G., Gruslys, A., Czarnecki, W. M., Zambaldi, V., Jaderberg, M., Lanctot, M., Sonnerat, N., Leibo, J. Z., Tuyls, K., and Graepel, T · 2018
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Reinforcement Learning: An Introduction
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A deep bayesian policy reuse approach against non-stationary agents
Zheng, Y., Meng, Z., Hao, J., Zhang, Z., Yang, T., and Fan, C · 2018
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Human-level performance in 3D multiplayer games with population-based reinforcement learning
Jaderberg, M., Czarnecki, W. M., Dunning, I., Marris, L., Lever, G., Castañeda, A. G., Beattie, C., Rabinowitz, N. C., Morcos, A. S., Ruderman, A., Sonnerat, N., Green, T., Deason, L., Leibo, J. Z., Silver, D., Hassabis, D., Kavukcuoglu, K., and Graepel, T · 2019
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An analytic theory of generalization dynamics and transfer learning in deep linear networks
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Maven: Multi-agent variational exploration
Mahajan, A., Rashid, T., Samvelyan, M., and Whiteson, S · 2019
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A survey on transfer learning for multiagent reinforcement learning systems
Silva, F. and Costa, A · 2019
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Efficient detection and optimal response against sophisticated opponents
Yang, T., Hao, J., Meng, Z., Zhang, C., Zheng, Y., and Zheng, Z · 2019
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The Hanabi challenge: A new frontier for AI research
Bard, N., Foerster, J. N., Chandar, S., Burch, N., Lanctot, M., Song, H. F., Parisotto, E., Dumoulin, V., Moitra, S., Hughes, E., Dunning, I., Mourad, S., Larochelle, H., Bellemare, M. G., and Bowling, M · 2020
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Iterative empirical game solving via single policy best response
Smith, M. O., Anthony, T., and Wellman, M. P · 2021
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