暂无分享,去创建一个
Joshua B. Tenenbaum | Tim Rocktäschel | Andres Campero | Roberta Raileanu | Heinrich Kuttler | Edward Grefenstette
[1] Tom Schaul,et al. Unifying Count-Based Exploration and Intrinsic Motivation , 2016, NIPS.
[2] Shane Legg,et al. IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures , 2018, ICML.
[3] Lei Han,et al. Curriculum-guided Hindsight Experience Replay , 2019, NeurIPS.
[4] Pieter Abbeel,et al. Reverse Curriculum Generation for Reinforcement Learning , 2017, CoRL.
[5] Pieter Abbeel,et al. Automatic Goal Generation for Reinforcement Learning Agents , 2017, ICML.
[6] John Foley,et al. ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents , 2018, ArXiv.
[7] Chrystopher L. Nehaniv,et al. Empowerment: a universal agent-centric measure of control , 2005, 2005 IEEE Congress on Evolutionary Computation.
[8] Edward Grefenstette,et al. TorchBeast: A PyTorch Platform for Distributed RL , 2019, ArXiv.
[9] Pierre-Yves Oudeyer,et al. Language Grounding through Social Interactions and Curiosity-Driven Multi-Goal Learning , 2019, ViGIL@NeurIPS.
[10] Alex Graves,et al. Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.
[11] Jivko Sinapov,et al. Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey , 2020, J. Mach. Learn. Res..
[12] Marc G. Bellemare,et al. Count-Based Exploration with Neural Density Models , 2017, ICML.
[13] Pierre-Yves Oudeyer,et al. What is Intrinsic Motivation? A Typology of Computational Approaches , 2007, Frontiers Neurorobotics.
[14] Pierre-Yves Oudeyer,et al. Intrinsic Motivation Systems for Autonomous Mental Development , 2007, IEEE Transactions on Evolutionary Computation.
[15] Sergey Levine,et al. Skew-Fit: State-Covering Self-Supervised Reinforcement Learning , 2019, ICML.
[16] Pierre-Yves Oudeyer,et al. Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments , 2019, CoRL.
[17] Tim Rocktäschel,et al. RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments , 2020, ICLR.
[18] Sham M. Kakade,et al. Towards Generalization and Simplicity in Continuous Control , 2017, NIPS.
[19] Jürgen Schmidhuber,et al. A possibility for implementing curiosity and boredom in model-building neural controllers , 1991 .
[20] Richard L. Lewis,et al. Internal Rewards Mitigate Agent Boundedness , 2010, ICML.
[21] Sergey Levine,et al. Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models , 2015, ArXiv.
[22] Michael L. Littman,et al. An analysis of model-based Interval Estimation for Markov Decision Processes , 2008, J. Comput. Syst. Sci..
[23] Ilya Kostrikov,et al. Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play , 2017, ICLR.
[24] Sebastian Risi,et al. Behind DeepMind’s AlphaStar AI that Reached Grandmaster Level in StarCraft II , 2020, KI - Künstliche Intelligenz.
[25] Julian Togelius,et al. Rotation, Translation, and Cropping for Zero-Shot Generalization , 2020, 2020 IEEE Conference on Games (CoG).
[26] Joelle Pineau,et al. A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning , 2018, ArXiv.
[27] Pieter Abbeel,et al. Automatic Curriculum Learning through Value Disagreement , 2020, NeurIPS.
[28] Satinder Singh,et al. On Learning Intrinsic Rewards for Policy Gradient Methods , 2018, NeurIPS.
[29] Edward Grefenstette,et al. RTFM: Generalising to Novel Environment Dynamics via Reading , 2020, ICLR.
[30] Marcin Andrychowicz,et al. Hindsight Experience Replay , 2017, NIPS.
[31] Jason Weston,et al. Curriculum learning , 2009, ICML '09.
[32] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[33] Edward Grefenstette,et al. The NetHack Learning Environment , 2020, NeurIPS.
[34] Andrew K. Lampinen,et al. Automated curricula through setter-solver interactions , 2019, ArXiv.
[35] Richard L. Lewis,et al. Where Do Rewards Come From , 2009 .
[36] Andrew G. Barto,et al. Intrinsic Motivation and Reinforcement Learning , 2013, Intrinsically Motivated Learning in Natural and Artificial Systems.
[37] Amos J. Storkey,et al. Exploration by Random Network Distillation , 2018, ICLR.
[38] John Schulman,et al. Leveraging Procedural Generation to Benchmark Reinforcement Learning , 2019, ICML.
[39] Pieter Abbeel,et al. Benchmarking Deep Reinforcement Learning for Continuous Control , 2016, ICML.
[40] Marlos C. Machado,et al. Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents , 2017, J. Artif. Intell. Res..
[41] Kevin Waugh,et al. DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker , 2017, ArXiv.
[42] Alexei A. Efros,et al. Curiosity-Driven Exploration by Self-Supervised Prediction , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[43] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[44] Pierre-Yves Oudeyer,et al. Automatic Curriculum Learning For Deep RL: A Short Survey , 2020, IJCAI.
[45] Jürgen Schmidhuber,et al. PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem , 2011, Front. Psychol..
[46] Michael J. Watts,et al. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information , 2020, IEEE Transactions on Neural Networks and Learning Systems.