“Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula”, Bronstein Et Al 2022

“Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula”⁠, Eli Bronstein, Sirish Srinivasan, Supratik Paul, Aman Sinha, Matthew O’Kelly, Payam Nikdel, Shimon Whiteson2022-12-02 (active learning⁠, robotics⁠, AI safety; similar)

“Melting Pot 2.0”, Agapiou Et Al 2022

“Melting Pot 2.0”⁠, John P. Agapiou, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán, Jayd Matyas, Yiran Mao, Peter Sunehag2022-11-24 (RNN⁠, MARL; similar)

“Token Turing Machines”, Ryoo Et Al 2022

“Token Turing Machines”⁠, Michael S. Ryoo, Keerthana Gopalakrishnan, Kumara Kahatapitiya, Ted Xiao, Kanishka Rao, Austin Stone2022-11-16 (compressed Transformer⁠, robotics; similar)

“Legged Locomotion in Challenging Terrains Using Egocentric Vision”, Agarwal Et Al 2022

“Legged Locomotion in Challenging Terrains using Egocentric Vision”⁠, Ananye Agarwal, Ashish Kumar, Jitendra Malik, Deepak Pathak2022-11-14 (RNN⁠, knowledge distillation⁠, robotics; similar; bibliography)

“Over-communicate No More: Situated RL Agents Learn Concise Communication Protocols”, Kalinowska Et Al 2022

“Over-communicate no more: Situated RL agents learn concise communication protocols”⁠, Aleksandra Kalinowska, Elnaz Davoodi, Florian Strub, Kory W. Mathewson, Ivana Kajic, Michael Bowling2022-11-02 (MARL; similar)

“Hyperbolic Deep Reinforcement Learning”, Cetin Et Al 2022

“Hyperbolic Deep Reinforcement Learning”⁠, Edoardo Cetin, Benjamin Chamberlain, Michael Bronstein, Jonathan J. Hunt2022-10-04 (similar; bibliography)

“Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning”, Lu Et Al 2022

“Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning”⁠, Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, Ashwin Kalyan2022-09-29 (ML dataset⁠, inner monologue (AI)⁠, math; similar)

“Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies With One Objective (ALM)”, Ghugare Et Al 2022

“Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective (ALM)”⁠, Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov2022-09-18 (model-based RL; similar; bibliography)

“Human-level Atari 200× Faster”, Kapturowski Et Al 2022

“Human-level Atari 200× faster”⁠, Steven Kapturowski, Víctor Campos, Ray Jiang, Nemanja Rakićević, Hado van Hasselt, Charles Blundell, Adrià Puigdomènech Badia2022-09-15 (knowledge distillation⁠, RL scaling; similar; bibliography)

“Nearest Neighbor Non-autoregressive Text Generation”, Niwa Et Al 2022

“Nearest Neighbor Non-autoregressive Text Generation”⁠, Ayana Niwa, Sho Takase, Naoaki Okazaki2022-08-26 (retrieval AI; similar)

“A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning”, Dann Et Al 2022

“A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning”⁠, Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert2022-08-23 (RL exploration⁠, Bayes; similar)

“Improved Policy Optimization for Online Imitation Learning”, Lavington Et Al 2022

“Improved Policy Optimization for Online Imitation Learning”⁠, Jonathan Wilder Lavington, Sharan Vaswani, Mark Schmidt2022-07-29 (preference learning; similar)

“Fine-grained Image Captioning With CLIP Reward”, Cho Et Al 2022

“Fine-grained Image Captioning with CLIP Reward”⁠, Jaemin Cho, Seunghyun Yoon, Ajinkya Kale, Franck Dernoncourt, Trung Bui, Mohit Bansal2022-05-26 (ML dataset⁠, CLIP; similar)

“Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi”, Grooten Et Al 2022

“Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi”⁠, Bram Grooten, Jelle Wemmenhove, Maurice Poot, Jim Portegies2022-03-22 (Hanabi AI; similar)

“Quantifying and Alleviating Political Bias in Language Models”, Liu Et Al 2022

“Quantifying and alleviating political bias in language models”⁠, Ruibo Liu, Chenyan Jia, Jason Wei, Guangxuan Xu, Soroush Vosoughi2022-03-01 (GPT⁠, sociology/tech; similar; bibliography)

“Machine Learning Helps Control Tokamak Plasmas”, Georgescu2022

“Retrieval-Augmented Reinforcement Learning”, Goyal Et Al 2022

“Retrieval-Augmented Reinforcement Learning”⁠, Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adria Puigdomenech Badia2022-02-17 (retrieval AI⁠, RNN; similar)

“Magnetic Control of Tokamak Plasmas through Deep Reinforcement Learning”, Degrave Et Al 2022

“Magnetic control of tokamak plasmas through deep reinforcement learning”⁠, Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan Tracey, Francesco Carpanese, Timo Ewalds2022-02-16 (science; similar; bibliography)

“A Data-driven Approach for Learning to Control Computers”, Humphreys Et Al 2022

“A data-driven approach for learning to control computers”⁠, Peter C. Humphreys, David Raposo, Toby Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson2022-02-16 (RL scaling⁠, tech; similar)

“Policy Learning and Evaluation With Randomized Quasi-Monte Carlo”, Arnold Et Al 2022

“Policy Learning and Evaluation with Randomized Quasi-Monte Carlo”⁠, Sebastien M. R. Arnold, Pierre L’Ecuyer, Liyu Chen, Yi-fan Chen, Fei Sha2022-02-16 (RL exploration⁠, power analysis⁠, statistics/probability; backlinks; similar)

“Why Should I Trust You, Bellman? The Bellman Error Is a Poor Replacement for Value Error”, Fujimoto Et Al 2022

“Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error”⁠, Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, Shixiang Shane Gu2022-01-28 (similar)

“Learning Dynamics and Generalization in Deep Reinforcement Learning”, Lyle Et Al 2022

“Learning Dynamics and Generalization in Deep Reinforcement Learning”⁠, Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal2022 (similar; bibliography)

“Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs”, Korkmaz2021

“Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs”⁠, Ezgi Korkmaz2021-12-16 (neural net/adversarial; similar)

“Amortized Noisy Channel Neural Machine Translation”, Pang Et Al 2021

“Amortized Noisy Channel Neural Machine Translation”⁠, Richard Yuanzhe Pang, He He, Kyunghyun Cho2021-12-16 (knowledge distillation⁠, GPT; similar)

“Simple but Effective: CLIP Embeddings for Embodied AI”, Khandelwal Et Al 2021

“Simple but Effective: CLIP Embeddings for Embodied AI”⁠, Apoorv Khandelwal, Luca Weihs, Roozbeh Mottaghi, Aniruddha Kembhavi2021-11-18 (CLIP⁠, robotics; similar)

“Recurrent Model-Free RL Is a Strong Baseline for Many POMDPs”, Ni Et Al 2021

“Recurrent Model-Free RL is a Strong Baseline for Many POMDPs”⁠, Tianwei Ni, Benjamin Eysenbach, Ruslan Salakhutdinov2021-10-11 (RNN; backlinks; similar)

“DroQ: Dropout Q-Functions for Doubly Efficient Reinforcement Learning”, Hiraoka Et Al 2021

“DroQ: Dropout Q-Functions for Doubly Efficient Reinforcement Learning”⁠, Takuya Hiraoka, Takahisa Imagawa, Taisei Hashimoto, Takashi Onishi, Yoshimasa Tsuruoka2021-10-05 (similar)

“Batch Size-invariance for Policy Optimization”, Hilton Et Al 2021

“Batch size-invariance for policy optimization”⁠, Jacob Hilton, Karl Cobbe, John Schulman2021-10-01 (similar)

“MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research”, Samvelyan Et Al 2021

“MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research”⁠, Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni2021-09-27 (ML dataset⁠, Nethack AI; similar)

“Bootstrapped Meta-Learning”, Flennerhag Et Al 2021

“Bootstrapped Meta-Learning”⁠, Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh2021-09-09 (RL exploration⁠, meta-learning; similar)

“Megaverse: Simulating Embodied Agents at One Million Experiences per Second”, Petrenko Et Al 2021

“Megaverse: Simulating Embodied Agents at One Million Experiences per Second”⁠, Aleksei Petrenko, Erik Wijmans, Brennan Shacklett, Vladlen Koltun2021-07-17 (CS/hardware⁠, MARL⁠, RL scaling; similar)

“Unbiased Gradient Estimation in Unrolled Computation Graphs With Persistent Evolution Strategies”, Vicol Et Al 2021

“Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies”⁠, Paul Vicol, Luke Metz, Jascha Sohl-Dickstein2021-07-01 (RNN⁠, neuroscience⁠, RL scaling; similar; bibliography)

“Multi-task Curriculum Learning in a Complex, Visual, Hard-exploration Domain: Minecraft”, Kanitscheider Et Al 2021

“Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft”⁠, Ingmar Kanitscheider, Joost Huizinga, David Farhi, William Hebgen Guss, Brandon Houghton, Raul Sampedro2021-06-28 (RL exploration; similar)

“On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning”, Vischer Et Al 2021

“On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning”⁠, Marc Aurel Vischer, Robert Tjarko Lange, Henning Sprekeler2021-05-04 (NN pruning⁠, RL exploration; similar)

“Podracer Architectures for Scalable Reinforcement Learning”, Hessel Et Al 2021

“Podracer architectures for scalable Reinforcement Learning”⁠, Matteo Hessel, Manuel Kroiss, Aidan Clark, Iurii Kemaev, John Quan, Thomas Keck, Fabio Viola, Hado van Hasselt2021-04-13 (AI scaling/hardware⁠, meta-learning⁠, MuZero⁠, RL scaling; similar; bibliography)

“Muesli: Combining Improvements in Policy Optimization”, Hessel Et Al 2021

“Muesli: Combining Improvements in Policy Optimization”⁠, Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber2021-04-13 (MuZero; backlinks; similar)

“Counter-Strike Deathmatch With Large-Scale Behavioural Cloning”, Pearce & Zhu2021

“Counter-Strike Deathmatch with Large-Scale Behavioural Cloning”⁠, Tim Pearce, Jun Zhu2021-04-09 (similar)

“Efficient Transformers in Reinforcement Learning Using Actor-Learner Distillation”, Parisotto & Salakhutdinov2021

“Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation”⁠, Emilio Parisotto, Ruslan Salakhutdinov2021-04-04 (RNN⁠, knowledge distillation⁠, self-attention; backlinks; similar)

“Large Batch Simulation for Deep Reinforcement Learning”, Shacklett Et Al 2021

“Large Batch Simulation for Deep Reinforcement Learning”⁠, Brennan Shacklett, Erik Wijmans, Aleksei Petrenko, Manolis Savva, Dhruv Batra, Vladlen Koltun, Kayvon Fatahalian2021-03-12 (AI scaling/hardware⁠, RL scaling; backlinks; similar)

“Reinforcement Learning for Datacenter Congestion Control”, Tessler Et Al 2021

“Reinforcement Learning for Datacenter Congestion Control”⁠, Chen Tessler, Yuval Shpigelman, Gal Dalal, Amit Mandelbaum, Doron Haritan Kazakov, Benjamin Fuhrer, Gal Chechik2021-02-18 (MARL; similar)

“How RL Agents Behave When Their Actions Are Modified”, Langlois & Everitt2021

“How RL Agents Behave When Their Actions Are Modified”⁠, Eric D. Langlois, Tom Everitt2021-02-15 (similar)

“Randomized Ensembled Double Q-Learning (REDQ): Learning Fast Without a Model”, Chen Et Al 2021

“Randomized Ensembled Double Q-Learning (REDQ): Learning Fast Without a Model”⁠, Xinyue Chen, Che Wang, Zijian Zhou, Keith Ross2021-01-15 (backlinks; similar)

“MLGO: a Machine Learning Guided Compiler Optimizations Framework”, Trofin Et Al 2021

“MLGO: a Machine Learning Guided Compiler Optimizations Framework”⁠, Mircea Trofin, Yundi Qian, Eugene Brevdo, Zinan Lin, Krzysztof Choromanski, David Li2021-01-13 (neural net⁠, CS/algorithm; similar)

“Evolving Reinforcement Learning Algorithms”, Co-Reyes Et Al 2021

“Evolving Reinforcement Learning Algorithms”⁠, John D. Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Sergey Levine, Quoc V. Le, Honglak Lee, Aleksandra Faust2021-01-08 (meta-learning; similar)

“Using Deep Reinforcement Learning to Reveal How the Brain Encodes Abstract State-space Representations in High-dimensional Environments”, Cross2020

“Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments”⁠, Logan Cross2020-12-15 (similar)

“Autonomous Navigation of Stratospheric Balloons Using Reinforcement Learning”, Bellemare Et Al 2020

“Autonomous navigation of stratospheric balloons using reinforcement learning”⁠, Marc G. Bellemare, Salvatore Candido, Pablo Samuel Castro, Jun Gong, Marlos C. Machado, Subhodeep Moitra2020-12-02 (robotics; similar)

“Offline Learning from Demonstrations and Unlabeled Experience”, Zolna Et Al 2020

“Offline Learning from Demonstrations and Unlabeled Experience”⁠, Konrad Zolna, Alexander Novikov, Ksenia Konyushkova, Caglar Gulcehre, Ziyu Wang, Yusuf Aytar, Misha Denil2020-11-27 (similar)

“A Unified Framework for Dopamine Signals across Timescales”, Kim Et Al 2020

“A Unified Framework for Dopamine Signals across Timescales”⁠, HyungGoo R. Kim, Athar N. Malik, John G. Mikhael, Pol Bech, Iku Tsutsui-Kimura, Fangmiao Sun, Yajun Zhang2020-11-27 (neuroscience; similar)

“Adversarial Vulnerabilities of Human Decision-making”, Dezfouli Et Al 2020

“Adversarial vulnerabilities of human decision-making”⁠, Amir Dezfouli, Richard Nock, Peter Dayan2020-11-04 (neural net/adversarial/human⁠, RNN⁠, decision theory; similar)

“Human-centric Dialog Training via Offline Reinforcement Learning”, Jaques Et Al 2020

“Human-centric Dialog Training via Offline Reinforcement Learning”⁠, Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Shane Gu2020-10-12 (RNN⁠, preference learning; similar)

“Emergent Social Learning via Multi-agent Reinforcement Learning”, Ndousse Et Al 2020

“Emergent Social Learning via Multi-agent Reinforcement Learning”⁠, Kamal Ndousse, Douglas Eck, Sergey Levine, Natasha Jaques2020-10-01 (MARL; similar)

“Super-Human Performance In Gran Turismo Sport Using Deep Reinforcement Learning”, Fuchs Et Al 2020

“Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning”⁠, Florian Fuchs, Yunlong Song, Elia Kaufmann, Davide Scaramuzza, Peter Duerr2020-08-18 (similar)

“Improving GAN Training With Probability Ratio Clipping and Sample Reweighting”, Wu Et Al 2020

“Improving GAN Training with Probability Ratio Clipping and Sample Reweighting”⁠, Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu2020-06-12 (GAN⁠, Transformer NN⁠, text style transfer; backlinks; similar)

“Conservative Q-Learning for Offline Reinforcement Learning”, Kumar Et Al 2020

“Conservative Q-Learning for Offline Reinforcement Learning”⁠, Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine2020-06-08 (similar)

“Controlling Overestimation Bias With Truncated Mixture of Continuous Distributional Quantile Critics (TQC)”, Kuznetsov Et Al 2020

“Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics (TQC)”⁠, Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin, Dmitry Vetrov2020-05-08 (similar)

“Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels”, Kostrikov Et Al 2020

“Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels”⁠, Ilya Kostrikov, Denis Yarats, Rob Fergus2020-04-28 (similar; bibliography)

“Evaluating the Rainbow DQN Agent in Hanabi With Unseen Partners”, Canaan Et Al 2020

“Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners”⁠, Rodrigo Canaan, Xianbo Gao, Youjin Chung, Julian Togelius, Andy Nealen, Stefan Menzel2020-04-28 (Hanabi AI; similar)

“Chip Placement With Deep Reinforcement Learning”, Mirhoseini Et Al 2020

“Chip Placement with Deep Reinforcement Learning”⁠, Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, Joe Jiang, Ebrahim Songhori, Shen Wang, Young-Joon Lee2020-04-22 (CS/hardware; similar)

“CURL: Contrastive Unsupervised Representations for Reinforcement Learning”, Srinivas Et Al 2020

“CURL: Contrastive Unsupervised Representations for Reinforcement Learning”⁠, Aravind Srinivas, Michael Laskin, Pieter Abbeel2020-04-08 (similar)

“Evolving Normalization-Activation Layers”, Liu Et Al 2020

“Evolving Normalization-Activation Layers”⁠, Hanxiao Liu, Andrew Brock, Karen Simonyan, Quoc V. Le2020-04-06 (CNN⁠, BigGAN⁠, StyleGAN; similar)

“Benchmarking End-to-End Behavioural Cloning on Video Games”, Kanervisto Et Al 2020

“Benchmarking End-to-End Behavioural Cloning on Video Games”⁠, Anssi Kanervisto, Joonas Pussinen, Ville Hautamäki2020-04-02 (backlinks; similar)

“Agent57: Outperforming the Atari Human Benchmark”, Badia Et Al 2020

“Agent57: Outperforming the Atari Human Benchmark”⁠, Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo2020-03-30 (RL exploration⁠, meta-learning; similar)

“Deep Neuroethology of a Virtual Rodent”, Merel Et Al 2020

“Deep neuroethology of a virtual rodent”⁠, Josh Merel, Diego Aldarondo, Jesse Marshall, Yuval Tassa, Greg Wayne, Bence Olveczky (DM/​Harvard)2020-03-11 (neuroscience⁠, robotics⁠, RL scaling; similar; bibliography)

“Causal Evidence Supporting the Proposal That Dopamine Transients Function As Temporal Difference Prediction Errors”, Maes Et Al 2020

“A Distributional Code for Value in Dopamine-based Reinforcement Learning”, Dabney Et Al 2020

“SEED RL: Scalable and Efficient Deep-RL With Accelerated Central Inference”, Espeholt Et Al 2019

“SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference”⁠, Lasse Espeholt, Raphaël Marinier, Piotr Stanczyk, Ke Wang, Marcin Michalski2019-10-15 (RNN; similar; bibliography)

“QUARL: Quantized Reinforcement Learning (ActorQ)”, Lam Et Al 2019

“QUARL: Quantized Reinforcement Learning (ActorQ)”⁠, Maximilian Lam, Sharad Chitlangia, Srivatsan Krishnan, Zishen Wan, Gabriel Barth-Maron, Aleksandra Faust2019-10-02 (reduced-precision NNs; similar; bibliography)

“Deep Learning Enables Rapid Identification of Potent DDR1 Kinase Inhibitors”, Zhavoronkov Et Al 2019

“Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the Playing Field”, Toromanoff Et Al 2019

“Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field”⁠, Marin Toromanoff, Emilie Wirbel, Fabien Moutarde2019-08-13 (backlinks; similar)

“A View on Deep Reinforcement Learning in System Optimization”, Haj-Ali Et Al 2019

“A View on Deep Reinforcement Learning in System Optimization”⁠, Ameer Haj-Ali, Nesreen K. Ahmed, Ted Willke, Joseph Gonzalez, Krste Asanovic, Ion Stoica2019-08-04 (CS/algorithm; backlinks; similar)

“Playing the Lottery With Rewards and Multiple Languages: Lottery Tickets in RL and NLP”, Yu Et Al 2019

“Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP”⁠, Haonan Yu, Sergey Edunov, Yuandong Tian, Ari S. Morcos2019-06-06 (RNN⁠, NN pruning; similar)

“Universal Quantum Control through Deep Reinforcement Learning”, Niu Et Al 2019

“Universal quantum control through deep reinforcement learning”⁠, Murphy Yuezhen Niu, Sergio Boixo, Vadim N. Smelyanskiy, Hartmut Neven2019-04-23 (neural net⁠, science; backlinks; similar)

“Reinforcement Learning for Recommender Systems: A Case Study on Youtube”, Chen2019

“Reinforcement Learning for Recommender Systems: A Case Study on Youtube”⁠, Minmin Chen2019-03-28 (neural net; similar)

“Benchmarking Classic and Learned Navigation in Complex 3D Environments”, Mishkin Et Al 2019

“Benchmarking Classic and Learned Navigation in Complex 3D Environments”⁠, Dmytro Mishkin, Alexey Dosovitskiy, Vladlen Koltun2019-01-30 (RL scaling; similar)

“AutoPhase: Compiler Phase-Ordering for High Level Synthesis With Deep Reinforcement Learning”, Haj-Ali Et Al 2019

“AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning”⁠, Ameer Haj-Ali, Qijing Huang, William Moses, John Xiang, Ion Stoica, Krste Asanovic, John Wawrzynek2019-01-15 (CS/algorithm; backlinks; similar)

“Designing Neural Networks through Neuroevolution”, Stanley Et Al 2019

“Reinforcement Learning in Artificial and Biological Systems”, Neftci & Averbeck2019

“Anxiety, Depression, and Decision Making: A Computational Perspective”, Bishop & Gagne2019

“Anxiety, Depression, and Decision Making: A Computational Perspective”⁠, Sonia J. Bishop, Christopher Gagne2019 (anxiety⁠, MDD⁠, neuroscience; similar)

“IRLAS: Inverse Reinforcement Learning for Architecture Search”, Guo Et Al 2018

“IRLAS: Inverse Reinforcement Learning for Architecture Search”⁠, Minghao Guo, Zhao Zhong, Wei Wu, Dahua Lin, Junjie Yan2018-12-13 (backlinks; similar)

“Top-K Off-Policy Correction for a REINFORCE Recommender System”, Chen Et Al 2018

“Top-K Off-Policy Correction for a REINFORCE Recommender System”⁠, Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed Chi2018-12-06 (retrieval AI⁠, order statistics/comparison; similar)

“Quantifying Generalization in Reinforcement Learning”, Cobbe Et Al 2018

“Quantifying Generalization in Reinforcement Learning”⁠, Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, John Schulman2018-12-06 (CNN⁠, RL scaling; similar)

“Relative Entropy Regularized Policy Iteration”, Abdolmaleki Et Al 2018

“Relative Entropy Regularized Policy Iteration”⁠, Abbas Abdolmaleki, Jost Tobias Springenberg, Jonas Degrave, Steven Bohez, Yuval Tassa, Dan Belov, Nicolas Heess2018-12-05 (similar)

“ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware”, Cai Et Al 2018

“ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware”⁠, Han Cai, Ligeng Zhu, Song Han2018-12-02 (CNN; backlinks; similar)

“Neural Probabilistic Motor Primitives for Humanoid Control”, Merel Et Al 2018

“Neural probabilistic motor primitives for humanoid control”⁠, Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess2018-11-28 (knowledge distillation⁠, autoencoder NN⁠, robotics; similar)

“InstaNAS: Instance-aware Neural Architecture Search”, Cheng Et Al 2018

“InstaNAS: Instance-aware Neural Architecture Search”⁠, An-Chieh Cheng, Chieh Hubert Lin, Da-Cheng Juan, Wei Wei, Min Sun2018-11-26 (backlinks; similar)

“A Closer Look at Deep Policy Gradients”, Ilyas Et Al 2018

“A Closer Look at Deep Policy Gradients”⁠, Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry2018-11-06 (similar)

“One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets With RL”, Paine Et Al 2018

“One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL”⁠, Tom Le Paine, Sergio Gómez Colmenarejo, Ziyu Wang, Scott Reed, Yusuf Aytar, Tobias Pfaff, Matt W. Hoffman2018-10-11 (RL scaling; similar)

“Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow”, Peng Et Al 2018

“Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow”⁠, Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, Sergey Levine2018-10-01 (StyleGAN⁠, video analysis; backlinks; similar)

“Learning to Perform Local Rewriting for Combinatorial Optimization”, Chen & Tian2018

“Learning to Perform Local Rewriting for Combinatorial Optimization”⁠, Xinyun Chen, Yuandong Tian2018-09-30 (CS/algorithm; backlinks; similar)

“R2D2: Recurrent Experience Replay in Distributed Reinforcement Learning”, Kapturowski Et Al 2018

“R2D2: Recurrent Experience Replay in Distributed Reinforcement Learning”⁠, Steven Kapturowski, Georg Ostrovski, John Quan, Remi Munos, Will Dabney2018-09-27 (RNN; similar; bibliography)

“Benchmarking Reinforcement Learning Algorithms on Real-World Robots”, Mahmood Et Al 2018

“Benchmarking Reinforcement Learning Algorithms on Real-World Robots”⁠, A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra2018-09-20 (robotics; similar)

“Deterministic Implementations for Reproducibility in Deep Reinforcement Learning”, Nagarajan Et Al 2018

“Deterministic Implementations for Reproducibility in Deep Reinforcement Learning”⁠, Prabhat Nagarajan, Garrett Warnell, Peter Stone2018-09-15 (CS/algorithm; backlinks; similar)

“Multi-task Deep Reinforcement Learning With PopArt”, Hessel Et Al 2018

“Multi-task Deep Reinforcement Learning with PopArt”⁠, Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, Hado van Hasselt2018-09-12 (similar)

“Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction”, Hashimoto & Tsuruoka2018

“Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction”⁠, Kazuma Hashimoto, Yoshimasa Tsuruoka2018-09-05 (neural net; backlinks; similar)

“Searching Toward Pareto-Optimal Device-Aware Neural Architectures”, Cheng Et Al 2018

“Searching Toward Pareto-Optimal Device-Aware Neural Architectures”⁠, An-Chieh Cheng, Jin-Dong Dong, Chi-Hung Hsu, Shu-Huan Chang, Min Sun, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen2018-08-29 (neural net; backlinks; similar)

“A Study of Reinforcement Learning for Neural Machine Translation”, Wu Et Al 2018

“A Study of Reinforcement Learning for Neural Machine Translation”⁠, Lijun Wu, Fei Tian, Tao Qin, Jianhuang Lai, Tie-Yan Liu2018-08-27 (neural net; backlinks; similar)

“Improving Abstraction in Text Summarization”, Kryściński Et Al 2018

“Improving Abstraction in Text Summarization”⁠, Wojciech Kryściński, Romain Paulus, Caiming Xiong, Richard Socher2018-08-23 (backlinks; similar)

“Learning to Optimize Join Queries With Deep Reinforcement Learning”, Krishnan Et Al 2018

“Learning to Optimize Join Queries With Deep Reinforcement Learning”⁠, Sanjay Krishnan, Zongheng Yang, Ken Goldberg, Joseph Hellerstein, Ion Stoica2018-08-09 (CS/algorithm; backlinks; similar)

“Is Q-learning Provably Efficient?”, Jin Et Al 2018

“Is Q-learning Provably Efficient?”⁠, Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, Michael I. Jordan2018-07-10 (RL exploration; similar)

“InfoNCE: Representation Learning With Contrastive Predictive Coding (CPC)”, Oord Et Al 2018

“InfoNCE: Representation Learning with Contrastive Predictive Coding (CPC)”⁠, Aaron van den Oord, Yazhe Li, Oriol Vinyals2018-07-10 (autoencoder NN; similar)

“Maximum a Posteriori Policy Optimisation”, Abdolmaleki Et Al 2018

“Maximum a Posteriori Policy Optimisation”⁠, Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, Martin Riedmiller2018-06-14 (similar)

“Resource-Efficient Neural Architect”, Zhou Et Al 2018

“Resource-Efficient Neural Architect”⁠, Yanqi Zhou, Siavash Ebrahimi, Sercan Ö. Arık, Haonan Yu, Hairong Liu, Greg Diamos2018-06-12 (backlinks; similar)

“The Unusual Effectiveness of Averaging in GAN Training”, Yazıcı Et Al 2018

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“Playing Atari With Six Neurons”, Cuccu Et Al 2018

“Playing Atari with Six Neurons”⁠, Giuseppe Cuccu, Julian Togelius, Philippe Cudre-Mauroux2018-06-04 (NN sparsity⁠, RL scaling; similar)

“Measuring the Intrinsic Dimension of Objective Landscapes”, Li Et Al 2018

“Measuring the Intrinsic Dimension of Objective Landscapes”⁠, Chunyuan Li, Heerad Farkhoor, Rosanne Liu, Jason Yosinski2018-04-24 (NN sparsity; similar)

“DP4G: Distributed Distributional Deterministic Policy Gradients”, Barth-Maron Et Al 2018

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“Optimizing Query Evaluations Using Reinforcement Learning for Web Search”, Rosset Et Al 2018

“Optimizing Query Evaluations using Reinforcement Learning for Web Search”⁠, Corby Rosset, Damien Jose, Gargi Ghosh, Bhaskar Mitra, Saurabh Tiwary2018-04-12 (CS/algorithm; backlinks; similar)

“Delayed Impact of Fair Machine Learning”, Liu Et Al 2018

“Delayed Impact of Fair Machine Learning”⁠, Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt2018-03-12 (decision theory; backlinks; similar)

“Learning Memory Access Patterns”, Hashemi Et Al 2018

“Learning Memory Access Patterns”⁠, Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis2018-03-06 (RNN⁠, CS/algorithm; backlinks; similar)

“Model-Ensemble Trust-Region Policy Optimization”, Kurutach Et Al 2018

“Model-Ensemble Trust-Region Policy Optimization”⁠, Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel2018-02-28 (similar)

“TD3: Addressing Function Approximation Error in Actor-Critic Methods”, Fujimoto Et Al 2018

“TD3: Addressing Function Approximation Error in Actor-Critic Methods”⁠, Scott Fujimoto, Herke van Hoof, David Meger2018-02-26 (backlinks; similar)

“Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration”, Liu Et Al 2018

“Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration”⁠, Evan Zheran Liu, Kelvin Guu, Panupong Pasupat, Tianlin Shi, Percy Liang2018-02-24 (RL exploration⁠, tech; backlinks; similar)

“Unicorn: Continual Learning With a Universal, Off-policy Agent”, Mankowitz Et Al 2018

“Unicorn: Continual Learning with a Universal, Off-policy Agent”⁠, Daniel J. Mankowitz, Augustin Žídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh2018-02-22 (similar)

“Efficient Neural Architecture Search via Parameter Sharing”, Pham Et Al 2018

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“IMPALA: Scalable Distributed Deep-RL With Importance Weighted Actor-Learner Architectures”, Espeholt Et Al 2018

“IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures”⁠, Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymir Mnih, Tom Ward, Yotam Doron, Vlad Firoiu2018-02-05 (CNN; similar)

“Regularized Evolution for Image Classifier Architecture Search”, Real Et Al 2018

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“Interactive Grounded Language Acquisition and Generalization in a 2D World”, Yu Et Al 2018

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“Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning With a Stochastic Actor”, Haarnoja Et Al 2018

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“Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning”, Such Et Al 2017

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“The Case for Learned Index Structures”, Kraska Et Al 2017

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“AI Safety Gridworlds”, Leike Et Al 2017

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“Classification With Costly Features Using Deep Reinforcement Learning”, Janisch Et Al 2017

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“Towards the Use of Deep Reinforcement Learning With Global Policy For Query-based Extractive Summarisation”, Molla2017

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“Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection”, Kato & Shinozaki2017

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“Gradient-free Policy Architecture Search and Adaptation”, Ebrahimi Et Al 2017

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“Swish: Searching for Activation Functions”, Ramachandran Et Al 2017

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“Rainbow: Combining Improvements in Deep Reinforcement Learning”, Hessel Et Al 2017

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“OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning”, Henderson Et Al 2017

“OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning”⁠, Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup2017-09-20 (GAN⁠, order statistics/comparison; backlinks; similar)

“Deep Reinforcement Learning That Matters”, Henderson Et Al 2017

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“Seq2SQL: Generating Structured Queries from Natural Language Using Reinforcement Learning”, Zhong Et Al 2017

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“The Successor Representation in Human Reinforcement Learning”, Momennejad Et Al 2017

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“Practical Block-wise Neural Network Architecture Generation”, Zhong Et Al 2017

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“Learning Policies for Adaptive Tracking With Deep Feature Cascades”, Huang Et Al 2017

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“Reinforced Video Captioning With Entailment Rewards”, Pasunuru & Bansal2017

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“A Distributional Perspective on Reinforcement Learning”, Bellemare Et Al 2017

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“Trial without Error: Towards Safe Reinforcement Learning via Human Intervention”, Saunders Et Al 2017

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“Tracking As Online Decision-Making: Learning a Policy from Streaming Videos With Reinforcement Learning”, III & Ramanan2017

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“Efficient Architecture Search by Network Transformation”, Cai Et Al 2017

“Efficient Architecture Search by Network Transformation”⁠, Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, Jun Wang2017-07-16 (CNN; backlinks; similar)

“Grammatical Error Correction With Neural Reinforcement Learning”, Sakaguchi Et Al 2017

“Grammatical Error Correction with Neural Reinforcement Learning”⁠, Keisuke Sakaguchi, Matt Post, Benjamin Van Durme2017-07-02 (backlinks; similar)

“Noisy Networks for Exploration”, Fortunato Et Al 2017

“Noisy Networks for Exploration”⁠, Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih2017-06-30 (RL exploration; similar)

“Gated-Attention Architectures for Task-Oriented Language Grounding”, Chaplot Et Al 2017

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“IDK Cascades: Fast Deep Learning by Learning Not to Overthink”, Wang Et Al 2017

“IDK Cascades: Fast Deep Learning by Learning not to Overthink”⁠, Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez2017-06-03 (backlinks; similar)

“Teaching Machines to Describe Images via Natural Language Feedback”, Ling & Fidler2017

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“Learning Time/Memory-Efficient Deep Architectures With Budgeted Super Networks”, Veniat & Denoyer2017

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“Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models”, Guimaraes Et Al 2017

“Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models”⁠, Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, Alán Aspuru-Guzik2017-05-30 (AI/music⁠, GAN; backlinks; similar)

“Ask the Right Questions: Active Question Reformulation With Reinforcement Learning”, Buck Et Al 2017

“Ask the Right Questions: Active Question Reformulation with Reinforcement Learning”⁠, Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby2017-05-22 (RL exploration; backlinks; similar)

“A Deep Reinforced Model for Abstractive Summarization”, Paulus Et Al 2017

“A Deep Reinforced Model for Abstractive Summarization”⁠, Romain Paulus, Caiming Xiong, Richard Socher2017-05-11 (RNN⁠, NN sampling; backlinks; similar)

“Inferring and Executing Programs for Visual Reasoning”, Johnson Et Al 2017

“Inferring and Executing Programs for Visual Reasoning”⁠, Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C. Lawrence Zitnick2017-05-10 (neural net; backlinks; similar)

“Time-Contrastive Networks: Self-Supervised Learning from Video”, Sermanet Et Al 2017

“Time-Contrastive Networks: Self-Supervised Learning from Video”⁠, Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine2017-04-23 (video analysis; backlinks; similar)

“RAM: Dynamic Computational Time for Visual Attention”, Li Et Al 2017

“RAM: Dynamic Computational Time for Visual Attention”⁠, Zhichao Li, Yi Yang, Xiao Liu, Feng Zhou, Shilei Wen, Wei Xu2017-03-30 (Transformer NN; backlinks; similar)

“End-to-end Optimization of Goal-driven and Visually Grounded Dialogue Systems”, Strub Et Al 2017

“End-to-end optimization of goal-driven and visually grounded dialogue systems”⁠, Florian Strub, Harm de Vries, Jeremie Mary, Bilal Piot, Aaron Courville, Olivier Pietquin2017-03-15 (backlinks; similar)

“Improving Neural Machine Translation With Conditional Sequence Generative Adversarial Nets”, Yang Et Al 2017

“Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets”⁠, Zhen Yang, Wei Chen, Feng Wang, Bo Xu2017-03-15 (GAN⁠, RNN⁠, NN sampling; backlinks; similar)

“Neural Episodic Control”, Pritzel Et Al 2017

“Neural Episodic Control”⁠, Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech, Oriol Vinyals, Demis Hassabis2017-03-06 (retrieval AI; similar)

“CoDeepNEAT: Evolving Deep Neural Networks”, Miikkulainen Et Al 2017

“CoDeepNEAT: Evolving Deep Neural Networks”⁠, Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju2017-03-01 (RL exploration; backlinks; similar)

“Tuning Recurrent Neural Networks With Reinforcement Learning”, Jaques Et Al 2017

“Tuning Recurrent Neural Networks with Reinforcement Learning”⁠, Natasha Jaques, Shixiang Gu, Richard E. Turner, Douglas Eck2017-02-14 (AI/music⁠, RNN⁠, NN sampling; backlinks; similar)

“PathNet: Evolution Channels Gradient Descent in Super Neural Networks”, Fernando Et Al 2017

“PathNet: Evolution Channels Gradient Descent in Super Neural Networks”⁠, Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A. Rusu, Alexander Pritzel2017-01-30 (similar)

“The Kelly Coin-Flipping Game: Exact Solutions”, Branwen Et Al 2017

“The Kelly Coin-Flipping Game: Exact Solutions”⁠, Gwern Branwen, Arthur Breitman, nshepperd, FeepingCreature, Gurkenglas2017-01-19 (C⁠, Haskell⁠, Python⁠, R⁠, shell⁠, model-based RL⁠, Bayes⁠, decision theory; backlinks; similar; bibliography)

“Deep Reinforcement Learning: A Brief Survey”, Arulkumaran Et Al 2017

“Loss Is Its Own Reward: Self-Supervision for Reinforcement Learning”, Shelhamer Et Al 2016

“Loss is its own Reward: Self-Supervision for Reinforcement Learning”⁠, Evan Shelhamer, Parsa Mahmoudieh, Max Argus, Trevor Darrell2016-12-21 (similar)

“Overcoming Catastrophic Forgetting in Neural Networks”, Kirkpatrick Et Al 2016

“Overcoming catastrophic forgetting in neural networks”⁠, James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu2016-12-02 (similar)

“Self-critical Sequence Training for Image Captioning”, Rennie Et Al 2016

“Self-critical Sequence Training for Image Captioning”⁠, Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, Jarret Ross, Vaibhava Goel2016-12-02 (neural net; backlinks; similar; bibliography)

“Reinforcement Learning With Unsupervised Auxiliary Tasks”, Jaderberg Et Al 2016

“Reinforcement Learning with Unsupervised Auxiliary Tasks”⁠, Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu2016-11-16 (similar)

“A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models”, Finn Et Al 2016

“A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models”⁠, Chelsea Finn, Paul Christiano, Pieter Abbeel, Sergey Levine2016-11-11 (GAN; backlinks; similar)

“Hybrid Computing Using a Neural Network With Dynamic External Memory”, Graves Et Al 2016

“Hybrid computing using a neural network with dynamic external memory”⁠, Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo2016-10-27 (RNN⁠, model-based RL; similar)

“Connecting Generative Adversarial Networks and Actor-Critic Methods”, Pfau & Vinyals2016

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“Deep Reinforcement Learning for Mention-Ranking Coreference Models”, Clark & Manning2016

“Deep Reinforcement Learning for Mention-Ranking Coreference Models”⁠, Kevin Clark, Christopher D. Manning2016-09-27 (backlinks; similar)

“Deep Neural Networks for YouTube Recommendations”, Covington Et Al 2016

“Deep Neural Networks for YouTube Recommendations”⁠, Paul Covington, Jay Adams, Emre Sargin2016-09-15 (retrieval AI; similar)

“The Malmo Platform for Artificial Intelligence Experimentation”, Johnson Et Al 2016

“The Malmo Platform for Artificial Intelligence Experimentation”⁠, Matthew Johnson, Katja Hofmann, Tim Hutton, David Bignell2016-07-01 (backlinks; similar)

“Progressive Neural Networks”, Rusu Et Al 2016

“Progressive Neural Networks”⁠, Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu2016-06-15 (similar)

“Learning to Optimize”, Li & Malik2016

“Learning to Optimize”⁠, Ke Li, Jitendra Malik2016-06-06 (MLP NN⁠, meta-learning; backlinks; similar)

“Deep Reinforcement Learning for Dialogue Generation”, Li Et Al 2016

“Deep Reinforcement Learning for Dialogue Generation”⁠, Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, Dan Jurafsky2016-06-05 (RNN; backlinks; similar)

“ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning”, Kempka Et Al 2016

“ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning”⁠, Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, Wojciech Jaśkowski2016-05-06 (CNN; backlinks; similar)

“Improving Information Extraction by Acquiring External Evidence With Reinforcement Learning”, Narasimhan Et Al 2016

“Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning”⁠, Karthik Narasimhan, Adam Yala, Regina Barzilay2016-03-25 (neural net; backlinks; similar)

“Asynchronous Methods for Deep Reinforcement Learning”, Mnih Et Al 2016

“Asynchronous Methods for Deep Reinforcement Learning”⁠, Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley2016-02-04 (similar)

“Dueling Network Architectures for Deep Reinforcement Learning”, Wang Et Al 2015

“Dueling Network Architectures for Deep Reinforcement Learning”⁠, Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas2015-11-20 (similar)

“Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning”, Parisotto Et Al 2015

“Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning”⁠, Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov2015-11-19 (knowledge distillation; similar)

“Prioritized Experience Replay”, Schaul Et Al 2015

“Prioritized Experience Replay”⁠, Tom Schaul, John Quan, Ioannis Antonoglou, David Silver2015-11-18 (similar)

“Deep Reinforcement Learning With Double Q-learning”, Hasselt Et Al 2015

“Deep Reinforcement Learning with Double Q-learning”⁠, Hado van Hasselt, Arthur Guez, David Silver2015-09-22 (similar)

“Gorila: Massively Parallel Methods for Deep Reinforcement Learning”, Nair Et Al 2015

“Gorila: Massively Parallel Methods for Deep Reinforcement Learning”⁠, Arun Nair, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Vedavyas Panneershelvam2015-07-15 (RL scaling; similar)

“Reinforcement Learning Neural Turing Machines—Revised”, Zaremba & Sutskever2015

“Reinforcement Learning Neural Turing Machines—Revised”⁠, Wojciech Zaremba, Ilya Sutskever2015-05-04 (RNN; backlinks; similar)

“An Invitation to Imitation”, Bagnell2015

“An Invitation to Imitation”⁠, J. Andrew Bagnell2015-03-14 (preference learning⁠, robotics; backlinks; similar)

“TRPO: Trust Region Policy Optimization”, Schulman Et Al 2015

“TRPO: Trust Region Policy Optimization”⁠, John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel2015-02-19 (backlinks; similar)

“DRAW: A Recurrent Neural Network For Image Generation”, Gregor Et Al 2015

“DRAW: A Recurrent Neural Network For Image Generation”⁠, Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra2015-02-16 (RNN⁠, neuroscience; backlinks; similar)

“Random Feedback Weights Support Learning in Deep Neural Networks”, Lillicrap Et Al 2014

“Random feedback weights support learning in deep neural networks”⁠, Timothy P. Lillicrap, Daniel Cownden, Douglas B. Tweed, Colin J. Akerman2014-11-02 (neuroscience; backlinks; similar)

“Learning to Execute”, Zaremba & Sutskever2014

“Learning to Execute”⁠, Wojciech Zaremba, Ilya Sutskever2014-10-17 (RNN; similar)

“Does Temporal Discounting Explain Unhealthy Behavior? A Systematic Review and Reinforcement Learning Perspective”, Story Et Al 2014

“Does temporal discounting explain unhealthy behavior? A systematic review and reinforcement learning perspective”⁠, Giles W. Story, Ivo Vlaev, Ben Seymour, Ara Darzi, Raymond J. Dolan2014 (personality; backlinks; similar)

“The Arcade Learning Environment: An Evaluation Platform for General Agents”, Bellemare Et Al 2012

“The Arcade Learning Environment: An Evaluation Platform for General Agents”⁠, Marc G. Bellemare, Yavar Naddaf, Joel Veness, Michael Bowling2012-07-19 (similar)

“Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting”, Xie Et Al 2012

“Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting”⁠, Ning Xie, Hirotaka Hachiya, Masashi Sugiyama2012-06-18 (neural net; backlinks; similar)

“Neural Mechanisms of Speed-accuracy Tradeoff”, Heitz & Schall2012

“Neural mechanisms of speed-accuracy tradeoff”⁠, Richard P. Heitz, Jeffrey D. Schall2012 (neuroscience; backlinks; similar)

“DAGGER: A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning”, Ross Et Al 2010

“DAGGER: A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning”⁠, Stephane Ross, Geoffrey J. Gordon, J. Andrew Bagnell2010-11-02 (preference learning; backlinks; similar)

“Midbrain Dopamine Neurons Encode a Quantitative Reward Prediction Error Signal”, Bayer & Glimcher2005

“Midbrain dopamine neurons encode a quantitative reward prediction error signal”⁠, Hannah M. Bayer, Paul W. Glimcher2005 (neuroscience; backlinks; similar)

“It Takes Two Neurons To Ride a Bicycle”, Cook2004

“It Takes Two Neurons To Ride a Bicycle”⁠, Matthew Cook2004-12-13 (backlinks; similar; bibliography)

“Learning to Drive a Bicycle Using Reinforcement Learning and Shaping”, Randløv & Alstrøm1998

“Learning to Drive a Bicycle Using Reinforcement Learning and Shaping”⁠, Jette Randløv, Preben Alstrøm1998 (backlinks)

“Descriptor Predictive Control: Tracking Controllers for a Riderless Bicycle”, Wissel Et Al 1996

“Descriptor predictive control: Tracking controllers for a riderless bicycle”⁠, D. von Wissel, R. Nikoukhah, F. Delebecque, S. L. Campbell1996-07-09 (backlinks; similar)

“Control for an Autonomous Bicycle”, Getz & Marsden1995

“Control for an autonomous bicycle”⁠, Neil H. Getz, Jerrold E. Marsden1995-05-21 (backlinks; similar)

“Simple Statistical Gradient-following Algorithms for Connectionist Reinforcement Learning”, Williams1992

“Simple statistical gradient-following algorithms for connectionist reinforcement learning”⁠, Ronald J. Williams1992 (backlinks; similar)

“Experiments on the Mechanization of Game-learning Part II. Rule-Based Learning and the Human Window [BOXES]”, Michie1982

“Experiments on the Mechanization of Game-learning Part I. Characterization of the Model and Its Parameters [MENACE]”, Michie1963

“A Matchbox Game-Learning Machine”, Gardner1962