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A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents.
Dealing with non-stationarity in multi-agent deep reinforcement learning
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Meta-learnt priors slow down catastrophic forgetting in neural networks
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Multi-agent Reinforcement Learning: An Overview , pp. 183–221
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Multi-agent learning with policy prediction
Zhang, C. and Lesser, V. R · 2010
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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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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
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High-dimensional continuous control using generalized advantage estimation
Schulman, J., Moritz, P., Levine, S., Jordan, M., and Abbeel, P · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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A survey of learning in multiagent environments: Dealing with non-stationarity
Modeling others using oneself in multi-agent reinforcement learning
Raileanu, R., Denton, E., Szlam, A., and Fergus, R · 2018
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Wei, E., Wicke, D., Freelan, D., and Luke, S · 2018
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Mean field multi-agent reinforcement learning
Yang, Y., Luo, R., Li, M., Zhou, M., Zhang, W., and Wang, J · 2018
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Meta-learning representations for continual learning
Javed, K. and White, M · 2019
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Stable opponent shaping in differentiable games
Letcher, A., Foerster, J., Balduzzi, D., Rocktäschel, T., and Whiteson, S · 2019
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Learning to learn without forgetting by maximizing transfer and minimizing interference
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Hernandez-Leal, P., Kaisers, M., Baarslag, T., and de Cote, E. M · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, O. P., and Mordatch, I · 2017
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A simple neural attentive meta-learner
Mishra, N., Rohaninejad, M., Chen, X., and Abbeel, P · 2017
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Continuous adaptation via meta-learning in nonstationary and competitive environments
Al-Shedivat, M., Bansal, T., Burda, Y., Sutskever, I., Mordatch, I., and Abbeel, P · 2018
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Learning policy representations in multiagent systems
Grover, A., Al-Shedivat, M., Gupta, J., Burda, Y., and Edwards, H · 2018
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Reptile: a scalable metalearning algorithm
Nichol, A. and Schulman, J · 2018
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Benchmarking deep reinforcement learning for continuous control
Duan, Y., Chen, X., Houthooft, R., Schulman, J., and Abbeel, P
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Riemer, M., Cases, I., Ajemian, R., Liu, M., Rish, I., Tu, Y., and Tesauro, G · 2019
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Probabilistic recursive reasoning for multi-agent reinforcement learning
Wen, Y., Yang, Y., Luo, R., Wang, J., and Pan, W · 2019
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Online fast adaptation and knowledge accumulation: a new approach to continual learning
Caccia, M., Rodriguez, P., Ostapenko, O., Normandin, F., Lin, M., Caccia, L., Laradji, I., Rish, I., Lacoste, A., Vazquez, D., et al · 2020
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Deep multi-agent reinforcement learning for decentralized continuous cooperative control
de Witt, C. S., Peng, B., Kamienny, P.-A., Torr, P., Böhmer, W., and Whiteson, S · 2020
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Meta-learning in neural networks: A survey
Hospedales, T., Antoniou, A., Micaelli, P., and Storkey, A · 2020
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Gradient surgery for multi-task learning
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