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Intelligent agents should have the ability to leverage knowledge from previously learned tasks in order to learn new ones quickly and efficiently.
The arcade learning environment: An evaluation platform for general agents
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Continuous control with deep reinforcement learning
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Matching networks for one shot learning
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r l 2 rl^{2} : Fast reinforcement learning via slow reinforcement learning
Duan, Y., J. Schulman, X. Chen, et al · 2016
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Initial progress in transfer for deep reinforcement learning algorithms
Du, Y., V. Gabriel, J. Irwin, et al · 2016
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Learning to reinforcement learn
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Optimization as a model for few-shot learning
Ravi, S., H. Larochelle · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., P. Abbeel, S. Levine · 2017
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One-shot visual imitation learning via meta-learning
Finn, C., T. Yu, T. Zhang, et al · 2017
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Proximal policy optimization algorithms
Schulman, J., F. Wolski, P. Dhariwal, et al · 2017
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Learning to learn: Meta-critic networks for sample efficient learning
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On first-order meta-learning algorithms
Nichol, A., J. Achiam, J. Schulman · 2018
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Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents
Machado, M. C., M. G. Bellemare, E. Talvitie, et al · 2018
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Rainbow: Combining improvements in deep reinforcement learning
Hessel, M., J. Modayil, H. Van Hasselt, et al · 2018
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Espeholt, L., H. Soyer, R. Munos, et al · 2018
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Addressing function approximation error in actor-critic methods
Fujimoto, S., H. Van Hoof, D. Meger · 2018
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Soft actor-critic algorithms and applications
Haarnoja, T., A. Zhou, K. Hartikainen, et al · 2018
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Visual analogies between atari games for studying transfer learning in rl
Sobol, D., L. Wolf, Y. Taigman · 2018
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Meta-gradient reinforcement learning
Xu, Z., H. van Hasselt, D. Silver · 2018
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Houthooft, R., R. Y. Chen, P. Isola, et al · 2018
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Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
Nagabandi, A., I. Clavera, S. Liu, et al · 2018
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Task-embedded control networks for few-shot imitation learning
James, S., M. Bloesch, A. J. Davison · 2018
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Evolved policy gradients
Houthooft, R., Y. Chen, P. Isola, et al · 2018
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A closer look at few-shot classification
Chen, W.-Y., Y.-C. Liu, Z. Kira, et al · 2019
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Efficient off-policy meta-reinforcement learning via probabilistic context variables, 2019
Rakelly, K., A. Zhou, D. Quillen, et al · 2019
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When to use parametric models in reinforcement learning?
van Hasselt, H. P., M. Hessel, J. Aslanides · 2019
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Visual transfer between atari games using competitive reinforcement learning
Mittel, A., P. Sowmya Munukutla · 2019
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Promp: Proximal meta-policy search
Rothfuss, J., D. Lee, I. Clavera, et al · 2019
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Modeling and optimization trade-off in meta-learning
Gao, K., O. Sener · 2020
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Meta-baseline: exploring simple meta-learning for few-shot learning
Chen, Y., Z. Liu, H. Xu, et al · 2021
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Towards more generalizable one-shot visual imitation learning
Mandi, Z., F. Liu, K. Lee, et al · 2021
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Mastering visual continuous control: Improved data-augmented reinforcement learning
Yarats, D., R. Fergus, A. Lazaric, et al · 2021
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Procedural generalization by planning with self-supervised world models
Anand, A., J. Walker, Y. Li, et al · 2021
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Kirsch, L., S. van Steenkiste, J. Schmidhuber · 2019
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A baseline for few-shot image classification
Dhillon, G. S., P. Chaudhari, A. Ravichandran, et al · 2020
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Rethinking few-shot image classification: a good embedding is all you need?
Tian, Y., Y. Wang, D. Krishnan, et al · 2020
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RLBench: The robot learning benchmark & learning environment
James, S., Z. Ma, D. Rovick Arrojo, et al · 2020
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Leveraging procedural generation to benchmark reinforcement learning
Cobbe, K., C. Hesse, J. Hilton, et al · 2020
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Transformer-based meta-imitation learning for robotic manipulation
Cachet, T., J. Perez · 2020
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Lee, S., S.-Y. Chung · 2021
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Hindsight task relabelling: Experience replay for sparse reward meta-rl
Packer, C., P. Abbeel, J. E. Gonzalez · 2021
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Exploration in approximate hyper-state space for meta reinforcement learning
Zintgraf, L. M., L. Feng, C. Lu, et al · 2021
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Metacure: Meta reinforcement learning with empowerment-driven exploration
Zhang, J., J. Wang, H. Hu, et al · 2021
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Introducing symmetries to black box meta reinforcement learning
Kirsch, L., S. Flennerhag, H. van Hasselt, et al · 2021
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Evolving reinforcement learning algorithms
Co-Reyes, J. D., Y. Miao, D. Peng, et al · 2021
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On the practical consistency of meta-reinforcement learning algorithms
Xiong, Z., L. M. Zintgraf, J. Beck, et al · 2021
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Multi-task reinforcement learning with context-based representations, 2021
Sodhani, S., A. Zhang, J. Pineau · 2021
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Mt-opt: Continuous multi-task robotic reinforcement learning at scale, 2021
Kalashnikov, D., J. Varley, Y. Chebotar, et al · 2021
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning, 2021
Yu, T., D. Quillen, Z. He, et al · 2021
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Stable-baselines3: Reliable reinforcement learning implementations
Raffin, A., A. Hill, A. Gleave, et al · 2021
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Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation
James, S., K. Wada, T. Laidlow, et al · 2022
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Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation
James, S., A. J. Davison · 2022
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In defense of the unitary scalarization for deep multi-task learning
Kurin, V., A. De Palma, I. Kostrikov, et al · 2022
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Pytorch implementation of rainbowdqn
Arulkumaran, K · 2022
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Coarse-to-Fine Q-attention with Learned Path Ranking
James, S., P. Abbeel · 2022
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Coarse-to-Fine Q-attention with Tree Expansion
James, S., P. Abbeel · 2022
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