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Current deep reinforcement learning (RL) algorithms are still highly task-specific and lack the ability to generalize to new environments.
Does the chimpanzee have a theory of mind?
Premack, D. and Woodruff, G · 1978
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A massively parallel architecture for a self-organizing neural pattern recognition machine
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Catastrophic interference in connectionist networks: The sequential learning problem
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Multi-agent reinforcement learning: Independent vs. cooperative agents
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Temporal difference learning and td-gammon
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Child: A first step towards continual learning
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Lifelong learning algorithms
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Ad hoc autonomous agent teams: Collaboration without pre-coordination
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The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M · 2013
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Goodfellow, I. J., Mirza, M., Xiao, D., Courville, A., and Bengio, Y · 2013
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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
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Schaul, T., Quan, J., Antonoglou, I., and Silver, D · 2015
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Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
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Vizdoom: A doom-based ai research platform for visual reinforcement learning
Kempka, M., Wydmuch, M., Runc, G., Toczek, J., and Jaśkowski, W · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
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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 · 2017
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Learning with opponent-learning awareness
Foerster, J. N., Chen, R. Y., Al-Shedivat, M., Whiteson, S., Abbeel, P., and Mordatch, I · 2017
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Benchmark environments for multitask learning in continuous domains
Henderson, P., Chang, W.-D., Shkurti, F., Hansen, J., Meger, D., and Dudek, G · 2017
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A deep policy inference q-network for multi-agent systems
Hong, Z.-W., Su, S.-Y., Shann, T.-Y., Chang, Y.-H., and Lee, C.-Y · 2017
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al · 2017
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Core50: a new dataset and benchmark for continuous object recognition
Lomonaco, V. and Maltoni, D · 2017
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Gradient episodic memory for continual learning
Lopez-Paz, D. and Ranzato, M · 2017
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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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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., et al · 2017
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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
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Simplified action decoder for deep multi-agent reinforcement learning
Hu, H. and Foerster, J. N · 2019
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Policy consolidation for continual reinforcement learning
Kaplanis, C., Shanahan, M., and Clopath, C · 2019
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Incremental object learning from contiguous views
Stojanov, S., Mishra, S., Thai, N. A., Dhanda, N., Humayun, A., Yu, C., Smith, L. B., and Rehg, J. M · 2019
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Multi-agent reinforcement learning: A selective overview of theories and algorithms
Zhang, K., Yang, Z., and Başar, T · 2019
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Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S · 2017
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Memory aware synapses: Learning what (not) to forget
Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., and Tuytelaars, T · 2018
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Cooperating with machines
Crandall, J. W., Oudah, M., Ishowo-Oloko, F., Abdallah, S., Bonnefon, J.-F., Cebrian, M., Shariff, A., Goodrich, M. A., Rahwan, I., et al · 2018
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Selective experience replay for lifelong learning
Isele, D. and Cosgun, A · 2018
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Continual reinforcement learning with complex synapses
Kaplanis, C., Shanahan, M., and Clopath, C · 2018
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Recurrent experience replay in distributed reinforcement learning
Kapturowski, S., Ostrovski, G., Quan, J., Munos, R., and Dabney, W · 2018
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Machine theory of mind
Rabinowitz, N., Perbet, F., Song, F., Zhang, C., Eslami, S. A., and Botvinick, M · 2018
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Fast context adaptation via meta-learning
Zintgraf, L., Shiarli, K., Kurin, V., Hofmann, K., and Whiteson, S · 2019
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Iirc: Incremental implicitly-refined classification
Abdelsalam, M., Faramarzi, M., Sodhani, S., and Chandar, S · 2020
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Defining benchmarks for continual few-shot learning
Antoniou, A., Patacchiola, M., Ochal, M., and Storkey, A · 2020
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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., et al · 2020
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” other-play” for zero-shot coordination
Hu, H., Lerer, A., Peysakhovich, A., and Foerster, J · 2020
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Towards continual reinforcement learning: A review and perspectives
Khetarpal, K., Riemer, M., Rish, I., and Precup, D · 2020
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Continual reinforcement learning in 3d non-stationary environments
Lomonaco, V., Desai, K., Culurciello, E., and Maltoni, D · 2020
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Understanding the role of training regimes in continual learning
Mirzadeh, S. I., Farajtabar, M., Pascanu, R., and Ghasemzadeh, H · 2020
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Jelly bean world: A testbed for never-ending learning
Platanios, E. A., Saparov, A., and Mitchell, T · 2020
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Gdumb: A simple approach that questions our progress in continual learning
Prabhu, A., Torr, P. H., and Dokania, P. K · 2020
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Stream-51: Streaming classification and novelty detection from videos
Roady, R., Hayes, T. L., Vaidya, H., and Kanan, C · 2020
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Are we ready for service robots? the openloris-scene datasets for lifelong slam, 2020
Shi, X., Li, D., Zhao, P., Tian, Q., Tian, Y., Long, Q., Zhu, C., Song, J., Qiao, F., Song, L., Guo, Y., Wang, Z., Zhang, Y., Qin, B., Yang, W., Wang, F., Chan, R. H. M., and She, Q · 2020
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Task-agnostic online reinforcement learning with an infinite mixture of gaussian processes, 2020
Xu, M., Ding, W., Zhu, J., Liu, Z., Chen, B., and Zhao, D · 2020
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