Timothy P. Lillicrap

137 papers A* 36A 2Journal 91Unranked 8

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents. ICLR Christopher Rawles, Sarah Clinckemaillie, Yifan Chang, Jonathan Waltz, Gabrielle Lau, Marybeth Fair, Alice Li, William E. Bishop, Wei Li, Folawiyo Campbell-Ajala, Daniel Kenji Toyama, Robert James Berry, Divya Tyamagundlu, Timothy P. Lillicrap, Oriana Riva
2025	J	`jnl`	Exploring exploration with foundation agents in interactive environments. Trans. Mach. Learn. Res. Daniel P. Sawyer, Nan Rosemary Ke, Hubert Soyer, Martin Engelcke, John Reid, David P. Reichert, Drew A. Hudson, Alexander Lerchner, Danilo Jimenez Rezende, Timothy P. Lillicrap, Michael Curtis Mozer, Jane X. Wang
2025	J	`jnl`	Mastering diverse control tasks through world models. Nat. Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy P. Lillicrap
2025	J	`jnl`	Societal Alignment Frameworks Can Improve LLM Alignment. CoRR Karolina Stanczak, Nicholas Meade, Mehar Bhatia, Hattie Zhou, Konstantin Böttinger, Jeremy Barnes, Jason Stanley, Jessica Montgomery, Richard S. Zemel, Nicolas Papernot, Nicolas Chapados, Denis Therien, Timothy P. Lillicrap, Ana Marasovic, Sylvie Delacroix, Gillian K. Hadfield, Siva Reddy
2025	J	`jnl`	Training Agents Inside of Scalable World Models. CoRR Danijar Hafner, Wilson Yan, Timothy P. Lillicrap
2024	J	`jnl`	AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents. CoRR Christopher Rawles, Sarah Clinckemaillie, Yifan Chang, Jonathan Waltz, Gabrielle Lau, Marybeth Fair, Alice Li, William E. Bishop, Wei Li, Folawiyo Campbell-Ajala, Daniel Toyama, Robert James Berry, Divya Tyamagundlu, Timothy P. Lillicrap, Oriana Riva
2024	J	`jnl`	Can foundation models actively gather information in interactive environments to test hypotheses? CoRR Nan Rosemary Ke, Danny P. Sawyer, Hubert Soyer, Martin Engelcke, David P. Reichert, Drew A. Hudson, John Reid, Alexander Lerchner, Danilo Jimenez Rezende, Timothy P. Lillicrap, Michael Mozer, Jane X. Wang
2024	J	`jnl`	Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. CoRR Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy P. Lillicrap, Jean-Baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, Ioannis Antonoglou, Rohan Anil, Sebastian Borgeaud, Andrew M. Dai, Katie Millican, Ethan Dyer, Mia Glaese, Thibault Sottiaux, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, James Molloy, Jilin Chen, Michael Isard, Paul Barham, Tom Hennigan, Ross McIlroy, Melvin Johnson, Johan Schalkwyk, Eli Collins, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, Clemens Meyer, Gregory Thornton, Zhen Yang, Henryk Michalewski, Zaheer Abbas, Nathan Schucher, Ankesh Anand, Richard Ives, James Keeling, Karel Lenc, Salem Haykal, Siamak Shakeri, Pranav Shyam, Aakanksha Chowdhery, Roman Ring, Stephen Spencer, Eren Sezener, et al.
2024	A*	`conf`	Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. NeurIPS Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P. Lillicrap, Danilo Jimenez Rezende, Yoshua Bengio, Michael C. Mozer, Sanjeev Arora
2024	J	`jnl`	Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. CoRR Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P. Lillicrap, Danilo J. Rezende, Yoshua Bengio, Michael Mozer, Sanjeev Arora
2024	J	`jnl`	Mixture-of-Depths: Dynamically allocating compute in transformer-based language models. CoRR David Raposo, Samuel Ritter, Blake A. Richards, Timothy P. Lillicrap, Peter Conway Humphreys, Adam Santoro
2024	J	`jnl`	Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning. CoRR Yuxi Xie, Anirudh Goyal, Wenyue Zheng, Min-Yen Kan, Timothy P. Lillicrap, Kenji Kawaguchi, Michael Shieh
2024	J	`jnl`	The refinement paradox and cumulative cultural evolution: Complex products of collective improvement favor conformist outcomes, blind copying, and hyper-credulity. PLoS Comput. Biol. Elena Miu, Luke Rendell, Sam Bowles, Rob Boyd, Daniel Cownden, Magnus Enquist, Kimmo Eriksson, Marcus W. Feldman, Timothy P. Lillicrap, Richard McElreath, Stuart Murray, James Ounsley, Kevin N. Lala
2023	J	`jnl`	Android in the Wild: A Large-Scale Dataset for Android Device Control. CoRR Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, Timothy P. Lillicrap
2023	A*	`conf`	AndroidInTheWild: A Large-Scale Dataset For Android Device Control. NeurIPS Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, Timothy P. Lillicrap
2023	A*	`conf`	Evaluating Long-Term Memory in 3D Mazes. ICLR Jurgis Pasukonis, Timothy P. Lillicrap, Danijar Hafner
2023	J	`jnl`	Mastering Diverse Domains through World Models. CoRR Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy P. Lillicrap
2022	A*	`conf`	A data-driven approach for learning to control computers. ICML Peter Conway Humphreys, David Raposo, Tobias Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Adam Santoro, Timothy P. Lillicrap
2022	J	`jnl`	A data-driven approach for learning to control computers. CoRR Peter Conway Humphreys, David Raposo, Toby Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Alex Goldin, Adam Santoro, Timothy P. Lillicrap
2022	J	`jnl`	Equilibrium Aggregation: Encoding Sets via Optimization. CoRR Sergey Bartunov, Fabian B. Fuchs, Timothy P. Lillicrap
2022	A	`conf`	Equilibrium aggregation: encoding sets via optimization. UAI Sergey Bartunov, Fabian B. Fuchs, Timothy P. Lillicrap
2022	J	`jnl`	Evaluating Long-Term Memory in 3D Mazes. CoRR Jurgis Pasukonis, Timothy P. Lillicrap, Danijar Hafner
2022	J	`jnl`	Evaluating Multimodal Interactive Agents. CoRR Josh Abramson, Arun Ahuja, Federico Carnevale, Petko Georgiev, Alex Goldin, Alden Hung, Jessica Landon, Timothy P. Lillicrap, Alistair Muldal, Blake A. Richards, Adam Santoro, Tamara von Glehn, Greg Wayne, Nathaniel Wong, Chen Yan
2022	J	`jnl`	Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback. CoRR Josh Abramson, Arun Ahuja, Federico Carnevale, Petko Georgiev, Alex Goldin, Alden Hung, Jessica Landon, Jirka Lhotka, Timothy P. Lillicrap, Alistair Muldal, George Powell, Adam Santoro, Guy Scully, Sanjana Srivastava, Tamara von Glehn, Greg Wayne, Nathaniel Wong, Chen Yan, Rui Zhu
2022	A*	`conf`	Intra-agent speech permits zero-shot task acquisition. NeurIPS Chen Yan, Federico Carnevale, Petko Georgiev, Adam Santoro, Aurelia Guy, Alistair Muldal, Chia-Chun Hung, Josh Abramson, Timothy P. Lillicrap, Gregory Wayne
2022	J	`jnl`	Intra-agent speech permits zero-shot task acquisition. CoRR Chen Yan, Federico Carnevale, Petko Georgiev, Adam Santoro, Aurelia Guy, Alistair Muldal, Chia-Chun Hung, Josh Abramson, Timothy P. Lillicrap, Gregory Wayne
2022	A*	`conf`	Large-Scale Retrieval for Reinforcement Learning. NeurIPS Peter Conway Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Theophane Weber, Timothy P. Lillicrap
2022	J	`jnl`	Large-Scale Retrieval for Reinforcement Learning. CoRR Peter Conway Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Théophane Weber, Timothy P. Lillicrap
2022	A*	`conf`	On the Stability and Scalability of Node Perturbation Learning. NeurIPS Naoki Hiratani, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham
2022	A*	`conf`	Retrieval-Augmented Reinforcement Learning. ICML Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter Conway Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell
2022	J	`jnl`	Retrieval-Augmented Reinforcement Learning. CoRR Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell
2022	J	`jnl`	The Brain-Computer Metaphor Debate Is Useless: A Matter of Semantics. Frontiers Comput. Sci. Blake A. Richards, Timothy P. Lillicrap
2022	J	`jnl`	Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution. CoRR Anthony Zador, Blake A. Richards, Bence Ölveczky, Sean Escola, Yoshua Bengio, Kwabena Boahen, Matthew M. Botvinick, Dmitri B. Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad P. Körding, Alexei A. Koulakov, Yann LeCun, Timothy P. Lillicrap, Adam H. Marblestone, Bruno A. Olshausen, Alexandre Pouget, Cristina Savin, Terrence J. Sejnowski, Eero P. Simoncelli, Sara A. Solla, David Sussillo, Andreas S. Tolias, Doris Tsao
2021	J	`jnl`	Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning. CoRR Josh Abramson, Arun Ahuja, Arthur Brussee, Federico Carnevale, Mary Cassin, Felix Fischer, Petko Georgiev, Alex Goldin, Tim Harley, Felix Hill, Peter Conway Humphreys, Alden Hung, Jessica Landon, Timothy P. Lillicrap, Hamza Merzic, Alistair Muldal, Adam Santoro, Guy Scully, Tamara von Glehn, Greg Wayne, Nathaniel Wong, Chen Yan, Rui Zhu
2021	A*	`conf`	Mastering Atari with Discrete World Models. ICLR Danijar Hafner, Timothy P. Lillicrap, Mohammad Norouzi, Jimmy Ba
2021	J	`jnl`	Symbolic Behaviour in Artificial Intelligence. CoRR Adam Santoro, Andrew K. Lampinen, Kory W. Mathewson, Timothy P. Lillicrap, David Raposo
2021	A*	`conf`	The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning. NeurIPS Shahab Bakhtiari, Patrick J. Mineault, Timothy P. Lillicrap, Christopher C. Pack, Blake A. Richards
2021	A*	`conf`	Towards Biologically Plausible Convolutional Networks. NeurIPS Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham
2021	J	`jnl`	Towards Biologically Plausible Convolutional Networks. CoRR Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham
2020	A*	`conf`	A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. NeurIPS Basile Confavreux, Friedemann Zenke, Everton J. Agnes, Timothy P. Lillicrap, Tim P. Vogels
2020	A*	`conf`	Automated curriculum generation through setter-solver interactions. ICLR Sébastien Racanière, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, Timothy P. Lillicrap
2020	J	`jnl`	Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban. CoRR Péter Karkus, Mehdi Mirza, Arthur Guez, Andrew Jaegle, Timothy P. Lillicrap, Lars Buesing, Nicolas Heess, Theophane Weber
2020	A*	`conf`	Compressive Transformers for Long-Range Sequence Modelling. ICLR Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, Timothy P. Lillicrap
2020	A*	`conf`	Dream to Control: Learning Behaviors by Latent Imagination. ICLR Danijar Hafner, Timothy P. Lillicrap, Jimmy Ba, Mohammad Norouzi
2020	J	`jnl`	Imitating Interactive Intelligence. CoRR Josh Abramson, Arun Ahuja, Arthur Brussee, Federico Carnevale, Mary Cassin, Stephen Clark, Andrew Dudzik, Petko Georgiev, Aurelia Guy, Tim Harley, Felix Hill, Alden Hung, Zachary Kenton, Jessica Landon, Timothy P. Lillicrap, Kory W. Mathewson, Alistair Muldal, Adam Santoro, Nikolay Savinov, Vikrant Varma, Greg Wayne, Nathaniel Wong, Chen Yan, Rui Zhu
2020	J	`jnl`	Mastering Atari with Discrete World Models. CoRR Danijar Hafner, Timothy P. Lillicrap, Mohammad Norouzi, Jimmy Ba
2020	J	`jnl`	Mastering Atari, Go, chess and shogi by planning with a learned model. Nat. Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy P. Lillicrap, David Silver
2020	A*	`conf`	Meta-Learning Deep Energy-Based Memory Models. ICLR Sergey Bartunov, Jack W. Rae, Simon Osindero, Timothy P. Lillicrap
2020	J	`jnl`	Physically Embedded Planning Problems: New Challenges for Reinforcement Learning. CoRR Mehdi Mirza, Andrew Jaegle, Jonathan J. Hunt, Arthur Guez, Saran Tunyasuvunakool, Alistair Muldal, Théophane Weber, Péter Karkus, Sébastien Racanière, Lars Buesing, Timothy P. Lillicrap, Nicolas Heess
2020	A*	`conf`	Training Generative Adversarial Networks by Solving Ordinary Differential Equations. NeurIPS Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli
2020	J	`jnl`	Training Generative Adversarial Networks by Solving Ordinary Differential Equations. CoRR Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andrew Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli
2020	J	`jnl`	dm_control: Software and Tasks for Continuous Control. CoRR Yuval Tassa, Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy P. Lillicrap, Nicolas Heess
2020	J	`jnl`	dm_control: Software and tasks for continuous control. Softw. Impacts Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy P. Lillicrap, Nicolas Heess, Yuval Tassa
2019	A*	`conf`	An Investigation of Model-Free Planning. ICML Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy P. Lillicrap
2019	J	`jnl`	An investigation of model-free planning. CoRR Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Théophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy P. Lillicrap
2019	J	`jnl`	Automated curricula through setter-solver interactions. CoRR Sébastien Racanière, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, Timothy P. Lillicrap
2019	A*	`conf`	Composing Entropic Policies using Divergence Correction. ICML Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess
2019	J	`jnl`	Compressive Transformers for Long-Range Sequence Modelling. CoRR Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Timothy P. Lillicrap
2019	A*	`conf`	Deep Compressed Sensing. ICML Yan Wu, Mihaela Rosca, Timothy P. Lillicrap
2019	J	`jnl`	Deep Compressed Sensing. CoRR Yan Wu, Mihaela Rosca, Timothy P. Lillicrap
2019	A*	`conf`	Deep Learning without Weight Transport. NeurIPS Mohamed Akrout, Collin Wilson, Peter Conway Humphreys, Timothy P. Lillicrap, Douglas B. Tweed
2019	J	`jnl`	Deep Learning without Weight Transport. CoRR Mohamed Akrout, Collin Wilson, Peter Conway Humphreys, Timothy P. Lillicrap, Douglas B. Tweed
2019	—	`conf`	Deep reinforcement learning with relational inductive biases. ICLR (Poster) Vinícius Flores Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David P. Reichert, Timothy P. Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew M. Botvinick, Oriol Vinyals, Peter W. Battaglia
2019	J	`jnl`	Dream to Control: Learning Behaviors by Latent Imagination. CoRR Danijar Hafner, Timothy P. Lillicrap, Jimmy Ba, Mohammad Norouzi
2019	—	`conf`	Episodic Curiosity through Reachability. ICLR (Poster) Nikolay Savinov, Anton Raichuk, Damien Vincent, Raphaël Marinier, Marc Pollefeys, Timothy P. Lillicrap, Sylvain Gelly
2019	A*	`conf`	Experience Replay for Continual Learning. NeurIPS David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Gregory Wayne
2019	J	`jnl`	Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nat. Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom Le Paine, Çaglar Gülçehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, David Silver
2019	J	`jnl`	Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette. CoRR Adam Santoro, Felix Hill, David G. T. Barrett, David Raposo, Matthew M. Botvinick, Timothy P. Lillicrap
2019	J	`jnl`	LOGAN: Latent Optimisation for Generative Adversarial Networks. CoRR Yan Wu, Jeff Donahue, David Balduzzi, Karen Simonyan, Timothy P. Lillicrap
2019	A*	`conf`	Learning Latent Dynamics for Planning from Pixels. ICML Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson
2019	—	`conf`	Learning to Make Analogies by Contrasting Abstract Relational Structure. ICLR (Poster) Felix Hill, Adam Santoro, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap
2019	J	`jnl`	Learning to Make Analogies by Contrasting Abstract Relational Structure. CoRR Felix Hill, Adam Santoro, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap
2019	J	`jnl`	Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. CoRR Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy P. Lillicrap, David Silver
2019	J	`jnl`	Meta-Learning Deep Energy-Based Memory Models. CoRR Sergey Bartunov, Jack W. Rae, Simon Osindero, Timothy P. Lillicrap
2019	A*	`conf`	Meta-Learning Neural Bloom Filters. ICML Jack W. Rae, Sergey Bartunov, Timothy P. Lillicrap
2019	J	`jnl`	Meta-Learning Neural Bloom Filters. CoRR Jack W. Rae, Sergey Bartunov, Timothy P. Lillicrap
2019	A	`conf`	Noise Contrastive Priors for Functional Uncertainty. UAI Danijar Hafner, Dustin Tran, Timothy P. Lillicrap, Alex Irpan, James Davidson
2019	—	`conf`	Recall Traces: Backtracking Models for Efficient Reinforcement Learning. ICLR (Poster) Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio
2019	J	`jnl`	What does it mean to understand a neural network? CoRR Timothy P. Lillicrap, Konrad P. Körding
2018	A*	`conf`	Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. NeurIPS Sergey Bartunov, Adam Santoro, Blake A. Richards, Luke Marris, Geoffrey E. Hinton, Timothy P. Lillicrap
2018	J	`jnl`	Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. CoRR Sergey Bartunov, Adam Santoro, Blake A. Richards, Geoffrey E. Hinton, Timothy P. Lillicrap
2018	J	`jnl`	DeepMind Control Suite. CoRR Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy P. Lillicrap, Martin A. Riedmiller
2018	—	`conf`	Distributed Distributional Deterministic Policy Gradients. ICLR (Poster) Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap
2018	J	`jnl`	Distributed Distributional Deterministic Policy Gradients. CoRR Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap
2018	J	`jnl`	Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction. CoRR Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess
2018	J	`jnl`	Episodic Curiosity through Reachability. CoRR Nikolay Savinov, Anton Raichuk, Raphaël Marinier, Damien Vincent, Marc Pollefeys, Timothy P. Lillicrap, Sylvain Gelly
2018	J	`jnl`	Experience Replay for Continual Learning. CoRR David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Greg Wayne
2018	A*	`conf`	Fast Parametric Learning with Activation Memorization. ICML Jack W. Rae, Chris Dyer, Peter Dayan, Timothy P. Lillicrap
2018	J	`jnl`	Fast Parametric Learning with Activation Memorization. CoRR Jack W. Rae, Chris Dyer, Peter Dayan, Timothy P. Lillicrap
2018	A*	`conf`	Learning Attractor Dynamics for Generative Memory. NeurIPS Yan Wu, Gregory Wayne, Karol Gregor, Timothy P. Lillicrap
2018	J	`jnl`	Learning Attractor Dynamics for Generative Memory. CoRR Yan Wu, Greg Wayne, Karol Gregor, Timothy P. Lillicrap
2018	J	`jnl`	Learning Latent Dynamics for Planning from Pixels. CoRR Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson
2018	A*	`conf`	Measuring abstract reasoning in neural networks. ICML Adam Santoro, Felix Hill, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap
2018	J	`jnl`	Measuring abstract reasoning in neural networks. CoRR David G. T. Barrett, Felix Hill, Adam Santoro, Ari S. Morcos, Timothy P. Lillicrap
2018	J	`jnl`	Optimizing Agent Behavior over Long Time Scales by Transporting Value. CoRR Chia-Chun Hung, Timothy P. Lillicrap, Josh Abramson, Yan Wu, Mehdi Mirza, Federico Carnevale, Arun Ahuja, Greg Wayne
2018	J	`jnl`	Recall Traces: Backtracking Models for Efficient Reinforcement Learning. CoRR Anirudh Goyal, Philemon Brakel, William Fedus, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio
2018	J	`jnl`	Relational Deep Reinforcement Learning. CoRR Vinícius Flores Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David P. Reichert, Timothy P. Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew M. Botvinick, Oriol Vinyals, Peter W. Battaglia
2018	A*	`conf`	Relational recurrent neural networks. NeurIPS Adam Santoro, Ryan Faulkner, David Raposo, Jack W. Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy P. Lillicrap
2018	J	`jnl`	Relational recurrent neural networks. CoRR Adam Santoro, Ryan Faulkner, David Raposo, Jack W. Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy P. Lillicrap
2018	J	`jnl`	Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors. CoRR Danijar Hafner, Dustin Tran, Alex Irpan, Timothy P. Lillicrap, James Davidson
2018	—	`conf`	The Kanerva Machine: A Generative Distributed Memory. ICLR (Poster) Yan Wu, Greg Wayne, Alex Graves, Timothy P. Lillicrap
2018	J	`jnl`	The Kanerva Machine: A Generative Distributed Memory. CoRR Yan Wu, Greg Wayne, Alex Graves, Timothy P. Lillicrap
2018	J	`jnl`	Unsupervised Predictive Memory in a Goal-Directed Agent. CoRR Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack W. Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Jimenez Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matthew M. Botvinick, Demis Hassabis, Timothy P. Lillicrap
2018	J	`jnl`	Vector-based navigation using grid-like representations in artificial agents. Nat. Andrea Banino, Caswell Barry, Benigno Uria, Charles Blundell, Timothy P. Lillicrap, Piotr Mirowski, Alexander Pritzel, Martin J. Chadwick, Thomas Degris, Joseph Modayil, Greg Wayne, Hubert Soyer, Fabio Viola, Brian Zhang, Ross Goroshin, Neil C. Rabinowitz, Razvan Pascanu, Charlie Beattie, Stig Petersen, Amir Sadik, Stephen Gaffney, Helen King, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Dharshan Kumaran
2017	J	`jnl`	A simple neural network module for relational reasoning. CoRR Adam Santoro, David Raposo, David G. T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter W. Battaglia, Timothy P. Lillicrap
2017	J	`jnl`	Data-efficient Deep Reinforcement Learning for Dexterous Manipulation. CoRR Ivaylo Popov, Nicolas Heess, Timothy P. Lillicrap, Roland Hafner, Gabriel Barth-Maron, Matej Vecerík, Thomas Lampe, Yuval Tassa, Tom Erez, Martin A. Riedmiller
2017	J	`jnl`	Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights. Neural Comput. Arash Samadi, Timothy P. Lillicrap, Douglas B. Tweed
2017	A*	`conf`	Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. ICRA Shixiang Gu, Ethan Holly, Timothy P. Lillicrap, Sergey Levine
2017	J	`jnl`	Discovering objects and their relations from entangled scene representations. CoRR David Raposo, Adam Santoro, David G. T. Barrett, Razvan Pascanu, Timothy P. Lillicrap, Peter W. Battaglia
2017	J	`jnl`	Generative Temporal Models with Memory. CoRR Mevlana Gemici, Chia-Chun Hung, Adam Santoro, Greg Wayne, Shakir Mohamed, Danilo Jimenez Rezende, David Amos, Timothy P. Lillicrap
2017	J	`jnl`	Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. CoRR Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine
2017	A*	`conf`	Learning to Learn without Gradient Descent by Gradient Descent. ICML Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matthew M. Botvinick, Nando de Freitas
2017	J	`jnl`	Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. CoRR David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy P. Lillicrap, Karen Simonyan, Demis Hassabis
2017	J	`jnl`	Mastering the game of Go without human knowledge. Nat. David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy P. Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis
2017	—	`conf`	Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. ICLR Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine
2017	J	`jnl`	StarCraft II: A New Challenge for Reinforcement Learning. CoRR Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John P. Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy P. Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, Rodney Tsing
2016	A*	`conf`	Asynchronous Methods for Deep Reinforcement Learning. ICML Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu
2016	J	`jnl`	Asynchronous Methods for Deep Reinforcement Learning. CoRR Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu
2016	A*	`conf`	Continuous Deep Q-Learning with Model-based Acceleration. ICML Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine
2016	J	`jnl`	Continuous Deep Q-Learning with Model-based Acceleration. CoRR Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine
2016	A*	`conf`	Continuous control with deep reinforcement learning. ICLR (Poster) Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra
2016	J	`jnl`	Deep Reinforcement Learning for Robotic Manipulation. CoRR Shixiang Gu, Ethan Holly, Timothy P. Lillicrap, Sergey Levine
2016	J	`jnl`	Learning and Transfer of Modulated Locomotor Controllers. CoRR Nicolas Heess, Gregory Wayne, Yuval Tassa, Timothy P. Lillicrap, Martin A. Riedmiller, David Silver
2016	J	`jnl`	Learning to Learn for Global Optimization of Black Box Functions. CoRR Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Nando de Freitas
2016	J	`jnl`	Mastering the game of Go with deep neural networks and tree search. Nat. David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis
2016	J	`jnl`	Matching Networks for One Shot Learning. CoRR Oriol Vinyals, Charles Blundell, Timothy P. Lillicrap, Koray Kavukcuoglu, Daan Wierstra
2016	A*	`conf`	Meta-Learning with Memory-Augmented Neural Networks. ICML Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap
2016	J	`jnl`	One-shot Learning with Memory-Augmented Neural Networks. CoRR Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap
2016	J	`jnl`	Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. CoRR Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine
2016	J	`jnl`	Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes. CoRR Jack W. Rae, Jonathan J. Hunt, Tim Harley, Ivo Danihelka, Andrew W. Senior, Greg Wayne, Alex Graves, Timothy P. Lillicrap
2015	—	`conf`	Learning Continuous Control Policies by Stochastic Value Gradients. NIPS Nicolas Heess, Gregory Wayne, David Silver, Timothy P. Lillicrap, Tom Erez, Yuval Tassa
2015	J	`jnl`	Learning Continuous Control Policies by Stochastic Value Gradients. CoRR Nicolas Heess, Greg Wayne, David Silver, Timothy P. Lillicrap, Yuval Tassa, Tom Erez
2015	J	`jnl`	Memory-based control with recurrent neural networks. CoRR Nicolas Heess, Jonathan J. Hunt, Timothy P. Lillicrap, David Silver
2015	J	`jnl`	Towards Principled Unsupervised Learning. CoRR Ilya Sutskever, Rafal Józefowicz, Karol Gregor, Danilo Jimenez Rezende, Timothy P. Lillicrap, Oriol Vinyals
2014	J	`jnl`	Random feedback weights support learning in deep neural networks. CoRR Timothy P. Lillicrap, Daniel Cownden, Douglas B. Tweed, Colin J. Akerman
2012	J	`jnl`	Relevance Realization and the Emerging Framework in Cognitive Science. J. Log. Comput. John Vervaeke, Timothy P. Lillicrap, Blake A. Richards
2008	J	`jnl`	Sensitivity Derivatives for Flexible Sensorimotor Learning. Neural Comput. M. N. Abdelghani, Timothy P. Lillicrap, Douglas B. Tweed