“MimicPlay: Long-Horizon Imitation Learning by Watching Human Play”, Wang Et Al 2023

“MimicPlay: Long-Horizon Imitation Learning by Watching Human Play”⁠, Chen Wang, Linxi Fan, Jiankai Sun, Ruohan Zhang, Li Fei-Fei, Danfei Xu, Yuke Zhu, Anima Anandkumar2023-02-24 (RL exploration⁠, robotics; similar)

“DreamerV3: Mastering Diverse Domains through World Models”, Hafner Et Al 2023

“DreamerV3: Mastering Diverse Domains through World Models”⁠, Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap2023-01-10 (RL exploration⁠, RL scaling; similar; bibliography)

“Merging Enzymatic and Synthetic Chemistry With Computational Synthesis Planning”, Levin Et Al 2022

“Merging enzymatic and synthetic chemistry with computational synthesis planning”⁠, Itai Levin, Mengjie Liu, Christopher A. Voigt, Connor W. Coley2022-12-14 (MLP NN⁠, biology; similar; bibliography)

“PALMER: Perception-Action Loop With Memory for Long-Horizon Planning”, Beker Et Al 2022

“PALMER: Perception-Action Loop with Memory for Long-Horizon Planning”⁠, Onur Beker, Mohammad Mohammadi, Amir Zamir2022-12-08 (similar)

“CICERO: Human-level Play in the Game Of Diplomacy By Combining Language Models With Strategic Reasoning”, Team Et Al 2022

“CICERO: Human-level play in the game of Diplomacy by combining language models with strategic reasoning”⁠, Meta Fundamental AI Research Diplomacy Team, Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina2022-11-22 (Diplomacy AI⁠, MARL; similar; bibliography)

“Online Learning and Bandits With Queried Hints”, Bhaskara Et Al 2022

“Online Learning and Bandits with Queried Hints”⁠, Aditya Bhaskara, Sreenivas Gollapudi, Sungjin Im, Kostas Kollias, Kamesh Munagala2022-11-04 (similar)

“Creating a Dynamic Quadrupedal Robotic Goalkeeper With Reinforcement Learning”, Huang Et Al 2022

“Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning”⁠, Xiaoyu Huang, Zhongyu Li, Yanzhen Xiang, Yiming Ni, Yufeng Chi, Yunhao Li, Lizhi Yang, Xue Bin Peng, Koushil Sreenath2022-10-10 (robotics; similar)

“Top-down Design of Protein Nanomaterials With Reinforcement Learning”, Lutz Et Al 2022

“Top-down design of protein nanomaterials with reinforcement learning”⁠, Isaac D. Lutz, Shunzhi Wang, Christoffer Norn, Andrew J. Borst, Yan Ting Zhao, Annie Dosey, Longxing Cao2022-09-25 (AlphaFold; 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-free RL; similar; bibliography)

“LaTTe: Language Trajectory TransformEr”, Bucker Et Al 2022

“LaTTe: Language Trajectory TransformEr”⁠, Arthur Bucker, Luis Figueredo, Sami Haddadin, Ashish Kapoor, Shuang Ma, Sai Vemprala, Rogerio Bonatti2022-08-04 (CLIP⁠, robotics; similar)

“Learning With Combinatorial Optimization Layers: a Probabilistic Approach”, Dalle Et Al 2022

“Learning with Combinatorial Optimization Layers: a Probabilistic Approach”⁠, Guillaume Dalle, Léo Baty, Louis Bouvier, Axel Parmentier2022-07-27 (neural net⁠, CS/algorithm; similar)

“PI-ARS: Accelerating Evolution-Learned Visual-Locomotion With Predictive Information Representations”, Lee Et Al 2022

“PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations”⁠, Kuang-Huei Lee, Ofir Nachum, Tingnan Zhang, Sergio Guadarrama, Jie Tan, Wenhao Yu2022-07-27 (RNN⁠, robotics; similar)

“Spatial Representation by Ramping Activity of Neurons in the Retrohippocampal Cortex”, Tennant Et Al 2022

“Spatial representation by ramping activity of neurons in the retrohippocampal cortex”⁠, Sarah A. Tennant, Harry Clark, Ian Hawes, Wing Kin Tam, Junji Hua, Wannan Yang, Klara Z. Gerlei, Emma R. Wood2022-07-26 (RNN⁠, neuroscience; similar)

“Inner Monologue: Embodied Reasoning through Planning With Language Models”, Huang Et Al 2022

“Inner Monologue: Embodied Reasoning through Planning with Language Models”⁠, Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson2022-07-12 (inner monologue (AI)⁠, PaLM⁠, robotics; similar; bibliography)

“LM-Nav: Robotic Navigation With Large Pre-Trained Models of Language, Vision, and Action”, Shah Et Al 2022

“LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action”⁠, Dhruv Shah, Blazej Osinski, Brian Ichter, Sergey Levine2022-07-10 (CLIP⁠, GPT⁠, robotics; backlinks; similar; bibliography)

“DayDreamer: World Models for Physical Robot Learning”, Wu Et Al 2022

“DayDreamer: World Models for Physical Robot Learning”⁠, Philipp Wu, Alejandro Escontrela, Danijar Hafner, Ken Goldberg, Pieter Abbeel2022-06-28 (robotics; similar; bibliography)

“Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos”, Baker Et Al 2022

“Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos”⁠, Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro2022-06-23 (video analysis⁠, RL exploration⁠, RL scaling; similar; bibliography)

“BYOL-Explore: Exploration by Bootstrapped Prediction”, Guo Et Al 2022

“BYOL-Explore: Exploration by Bootstrapped Prediction”⁠, Zhaohan Daniel Guo, Shantanu Thakoor, Miruna Pîslar, Bernardo Avila Pires, Florent Altché, Corentin Tallec2022-06-16 (RNN⁠, RL exploration; similar)

“Director: Deep Hierarchical Planning from Pixels”, Hafner Et Al 2022

“Director: Deep Hierarchical Planning from Pixels”⁠, Danijar Hafner, Kuang-Huei Lee, Ian Fischer, Pieter Abbeel2022-06-08 (reduced-precision NNs⁠, RL exploration; similar; bibliography)

“Flexible Diffusion Modeling of Long Videos”, Harvey Et Al 2022

“Flexible Diffusion Modeling of Long Videos”⁠, William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach, Frank Wood2022-05-23 (ML dataset⁠, diffusion model⁠, video generation⁠, RL scaling; similar)

“Housekeep: Tidying Virtual Households Using Commonsense Reasoning”, Kant Et Al 2022

“Housekeep: Tidying Virtual Households using Commonsense Reasoning”⁠, Yash Kant, Arun Ramachandran, Sriram Yenamandra, Igor Gilitschenski, Dhruv Batra, Andrew Szot, Harsh Agrawal2022-05-22 (ML dataset⁠, Transformer NN⁠, preference learning⁠, robotics; backlinks; similar)

“Semantic Exploration from Language Abstractions and Pretrained Representations”, Tam Et Al 2022

“Semantic Exploration from Language Abstractions and Pretrained Representations”⁠, Allison C. Tam, Neil C. Rabinowitz, Andrew K. Lampinen, Nicholas A. Roy, Stephanie C. Y. Chan, D. J. Strouse2022-04-08 (CLIP⁠, RL exploration⁠, robotics; similar; bibliography)

“Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning”, Valassakis Et Al 2022

“Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning”⁠, Eugene Valassakis, Georgios Papagiannis, Norman Di Palo, Edward Johns2022-04-06 (robotics; similar)

“Do As I Can, Not As I Say (SayCan): Grounding Language in Robotic Affordances”, Ahn Et Al 2022

“Do As I Can, Not As I Say (SayCan): Grounding Language in Robotic Affordances”⁠, Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan2022-04-04 (PaLM⁠, robotics⁠, AI safety⁠, RL scaling; similar; bibliography)

“Reinforcement Learning With Action-Free Pre-Training from Videos”, Seo Et Al 2022

“Reinforcement Learning with Action-Free Pre-Training from Videos”⁠, Younggyo Seo, Kimin Lee, Stephen James, Pieter Abbeel2022-03-25 (video analysis⁠, video generation; similar)

“On-the-fly Strategy Adaptation for Ad-hoc Agent Coordination”, Zand Et Al 2022

“On-the-fly Strategy Adaptation for ad-hoc Agent Coordination”⁠, Jaleh Zand, Jack Parker-Holder, Stephen J. Roberts2022-03-08 (Hanabi AI⁠, Bayes; similar)

“VAPO: Affordance Learning from Play for Sample-Efficient Policy Learning”, Borja-Diaz Et Al 2022

“VAPO: Affordance Learning from Play for Sample-Efficient Policy Learning”⁠, Jessica Borja-Diaz, Oier Mees, Gabriel Kalweit, Lukas Hermann, Joschka Boedecker, Wolfram Burgard2022-03-01 (RL exploration⁠, robotics; similar)

“Learning Synthetic Environments and Reward Networks for Reinforcement Learning”, Ferreira Et Al 2022

“Learning Synthetic Environments and Reward Networks for Reinforcement Learning”⁠, Fabio Ferreira, Thomas Nierhoff, Andreas Saelinger, Frank Hutter2022-02-06 (meta-learning; similar)

“LID: Pre-Trained Language Models for Interactive Decision-Making”, Li Et Al 2022

“LID: Pre-Trained Language Models for Interactive Decision-Making”⁠, Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek2022-02-03 (GPT⁠, RL exploration⁠, robotics; backlinks; similar)

“How to Build a Cognitive Map: Insights from Models of the Hippocampal Formation”, Whittington Et Al 2022

“How to build a cognitive map: insights from models of the hippocampal formation”⁠, James C. R. Whittington, David McCaffary, Jacob J. W. Bakermans, Timothy E. J. Behrens2022-02-03 (neuroscience; similar)

“Language Models As Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents”, Huang Et Al 2022

“Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents”⁠, Wenlong Huang, Pieter Abbeel, Deepak Pathak, Igor Mordatch2022-01-18 (GPT⁠, robotics; similar)

“What Is the Point of Computers? A Question for Pure Mathematicians”, Buzzard2021

“What is the point of computers? A question for pure mathematicians”⁠, Kevin Buzzard2021-12-22 (economics/automation⁠, math)

“An Experimental Design Perspective on Model-Based Reinforcement Learning”, Mehta Et Al 2021

“An Experimental Design Perspective on Model-Based Reinforcement Learning”⁠, Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger2021-12-09 (RL exploration⁠, Bayes; similar)

“Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates”, Kantack2021

“Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates”⁠, Nicholas Kantack2021-11-18 (Hanabi AI; similar)

“Learning Representations for Pixel-based Control: What Matters and Why?”, Tomar Et Al 2021

“Learning Representations for Pixel-based Control: What Matters and Why?”⁠, Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor2021-11-15 (similar)

“Learning Behaviors through Physics-driven Latent Imagination”, Richard Et Al 2021

“Learning Behaviors through Physics-driven Latent Imagination”⁠, Antoine Richard, Stephanie Aravecchia, Matthieu Geist, Cédric Pradalier2021-11-04 (robotics; similar)

“Is Bang-Bang Control All You Need? Solving Continuous Control With Bernoulli Policies”, Seyde Et Al 2021

“Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies”⁠, Tim Seyde, Igor Gilitschenski, Wilko Schwarting, Bartolomeo Stellato, Martin Riedmiller, Markus Wulfmeier2021-11-03 (similar)

“Skill Induction and Planning With Latent Language”, Sharma Et Al 2021

“Skill Induction and Planning with Latent Language”⁠, Pratyusha Sharma, Antonio Torralba, Jacob Andreas2021-10-04 (Transformer NN⁠, robotics; similar)

“Brax—A Differentiable Physics Engine for Large Scale Rigid Body Simulation”, Freeman Et Al 2021

“Brax—A Differentiable Physics Engine for Large Scale Rigid Body Simulation”⁠, C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem2021-06-24 (CS/hardware⁠, RL scaling; similar; bibliography)

“FitVid: Overfitting in Pixel-Level Video Prediction”, Babaeizadeh Et Al 2021

“FitVid: Overfitting in Pixel-Level Video Prediction”⁠, Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, Dumitru Erhan2021-06-24 (video generation; similar)

“A Graph Placement Methodology for Fast Chip Design”, Mirhoseini Et Al 2021

“The Whole Prefrontal Cortex Is Premotor Cortex”, Fine & Hayden2021

“The whole prefrontal cortex is premotor cortex”⁠, Justin M. Fine, Benjamin Y. Hayden2021-06-08 (neuroscience; similar)

“PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World”, Zellers Et Al 2021

“PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World”⁠, Rowan Zellers, Ari Holtzman, Matthew Peters, Roozbeh Mottaghi, Aniruddha Kembhavi, Ali Farhadi, Yejin Choi2021-06-01 (RL scaling; backlinks; similar)

“Constructions in Combinatorics via Neural Networks”, Wagner2021

“Constructions in combinatorics via neural networks”⁠, Adam Zsolt Wagner2021-04-29 (math)

“Machine Translation Decoding beyond Beam Search”, Leblond Et Al 2021

“Machine Translation Decoding beyond Beam Search”⁠, Rémi Leblond, Jean-Baptiste Alayrac, Laurent Sifre, Miruna Pislar, Jean-Baptiste Lespiau, Ioannis Antonoglou2021-04-12 (NN sampling⁠, Transformer NN; similar)

“Replaying Real Life: How the Waymo Driver Avoids Fatal Human Crashes”, Waymo2021

“Replaying real life: how the Waymo Driver avoids fatal human crashes”⁠, Waymo2021-03-08 (robotics⁠, AI safety; backlinks; similar; bibliography)

“Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing”, Brunnbauer Et Al 2021

“Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing”⁠, Axel Brunnbauer, Luigi Berducci, Andreas Brandstätter, Mathias Lechner, Ramin Hasani, Daniela Rus, Radu Grosu2021-03-08 (robotics; similar)

“Waymo Simulated Driving Behavior in Reconstructed Fatal Crashes within an Autonomous Vehicle Operating Domain”, Scanlon Et Al 2021

“Waymo Simulated Driving Behavior in Reconstructed Fatal Crashes within an Autonomous Vehicle Operating Domain”⁠, John M. Scanlon, Kristofer D. Kusano, Tom Daniel, Christopher Alderson, Alexander Ogle, Trent Victor2021-03-08 (robotics⁠, AI safety; backlinks; similar)

“COMBO: Conservative Offline Model-Based Policy Optimization”, Yu Et Al 2021

“COMBO: Conservative Offline Model-Based Policy Optimization”⁠, Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn2021-02-16 (similar)

“Solving Mixed Integer Programs Using Neural Networks”, Nair Et Al 2020

“Solving Mixed Integer Programs Using Neural Networks”⁠, Vinod Nair, Sergey Bartunov, Felix Gimeno, Ingrid von Glehn, Pawel Lichocki, Ivan Lobov, Brendan O’Donoghue2020-12-23 (neural net⁠, decision theory; similar)

“ViNG: Learning Open-World Navigation With Visual Goals”, Shah Et Al 2020

“ViNG: Learning Open-World Navigation with Visual Goals”⁠, Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine2020-12-17 (robotics; backlinks; similar)

“Targeting for Long-term Outcomes”, Yang Et Al 2020

“Targeting for long-term outcomes”⁠, Jeremy Yang, Dean Eckles, Paramveer Dhillon, Sinan Aral2020-10-29 (decision theory; similar)

“A Time Leap Challenge for SAT Solving”, Fichte Et Al 2020

“A Time Leap Challenge for SAT Solving”⁠, Johannes K. Fichte, Markus Hecher, Stefan Szeider2020-08-05 (AI⁠, CS/algorithm⁠, CS/hardware⁠, experience curves; backlinks)

“RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning”, Gulcehre Et Al 2020

“RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning”⁠, Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gomez Colmenarejo, Konrad Zolna, Rishabh Agarwal2020-06-24 (backlinks; similar)

“Learning to Simulate Dynamic Environments With GameGAN”, Kim Et Al 2020

“Learning to Simulate Dynamic Environments with GameGAN”⁠, Seung Wook Kim, Yuhao Zhou, Jonah Philion, Antonio Torralba, Sanja Fidler2020-05-25 (GAN; similar)

“Learning to Simulate Dynamic Environments With GameGAN [homepage]”, Kim Et Al 2020

“Learning to Simulate Dynamic Environments with GameGAN [homepage]”⁠, Seung Wook Kim, Yuhao Zhou, Jonah Philion, Antonio Torralba, Sanja Fidler2020-05 (GAN; similar)

“Reinforcement Learning With Augmented Data”, Laskin Et Al 2020

“Reinforcement Learning with Augmented Data”⁠, Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas2020-04-30 (CNN; similar)

“Learning to Fly via Deep Model-Based Reinforcement Learning”, Becker-Ehmck Et Al 2020

“Learning to Fly via Deep Model-Based Reinforcement Learning”⁠, Philip Becker-Ehmck, Maximilian Karl, Jan Peters, Patrick van der Smagt2020-03-19 (robotics; similar)

“Introducing Dreamer: Scalable Reinforcement Learning Using World Models”, Hafner2020

“Introducing Dreamer: Scalable Reinforcement Learning Using World Models”⁠, Danijar Hafner2020-03-18 (similar; bibliography)

“Learning to Prove Theorems by Learning to Generate Theorems”, Wang & Deng2020

“Learning to Prove Theorems by Learning to Generate Theorems”⁠, Mingzhe Wang, Jia Deng2020-02-17 (math; backlinks; similar)

“The Gambler’s Problem and Beyond”, Wang Et Al 2019

“The Gambler’s Problem and Beyond”⁠, Baoxiang Wang, Shuai Li, Jiajin Li, Siu On Chan2019-12-31 (decision theory; backlinks; similar)

“Dream to Control: Learning Behaviors by Latent Imagination”, Hafner Et Al 2019

“Dream to Control: Learning Behaviors by Latent Imagination”⁠, Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi2019-12-03 (similar)

“Approximate Inference in Discrete Distributions With Monte Carlo Tree Search and Value Functions”, Buesing Et Al 2019

“Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions”⁠, Lars Buesing, Nicolas Heess, Theophane Weber2019-10-15 (neural net⁠, Bayes; backlinks; similar)

“Designing Agent Incentives to Avoid Reward Tampering”, Everitt Et Al 2019

“Designing agent incentives to avoid reward tampering”⁠, Tom Everitt, Ramana Kumar, Marcus Hutter2019-08-14 (AI safety⁠, statistics/causality; backlinks; similar)

“An Application of Reinforcement Learning to Aerobatic Helicopter Flight”, Abbeel Et Al 2019

“An Application of Reinforcement Learning to Aerobatic Helicopter Flight”⁠, Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng2019-07-16 (robotics; similar)

“When to Trust Your Model: Model-Based Policy Optimization (MOPO)”, Janner Et Al 2019

“When to Trust Your Model: Model-Based Policy Optimization (MOPO)”⁠, Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine2019-06-19 (backlinks; similar)

“Fast Task Inference With Variational Intrinsic Successor Features”, Hansen Et Al 2019

“Fast Task Inference with Variational Intrinsic Successor Features”⁠, Steven Hansen, Will Dabney, Andre Barreto, Tom Van de Wiele, David Warde-Farley, Volodymyr Mnih2019-06-12 (similar)

“Bayesian Layers: A Module for Neural Network Uncertainty”, Tran Et Al 2018

“Bayesian Layers: A Module for Neural Network Uncertainty”⁠, Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner2018-12-10 (RNN⁠, Transformer NN⁠, AI scaling/hardware⁠, RL scaling⁠, Bayes; similar)

“PlaNet: Learning Latent Dynamics for Planning from Pixels”, Hafner Et Al 2018

“PlaNet: Learning Latent Dynamics for Planning from Pixels”⁠, Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson2018-11-12 (similar)

“Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning”, Foerster Et Al 2018

“Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning”⁠, Jakob N. Foerster, Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew Botvinick2018-11-04 (Hanabi AI⁠, Bayes; similar)

“Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search”, Zela Et Al 2018

“Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search”⁠, Arber Zela, Aaron Klein, Stefan Falkner, Frank Hutter2018-07-18 (neural net; backlinks; similar)

“The Alignment Problem for Bayesian History-Based Reinforcement Learners”, Everitt & Hutter2018

“The Alignment Problem for Bayesian History-Based Reinforcement Learners”⁠, Tom Everitt, Marcus Hutter2018-06-22 (AI safety⁠, Bayes; similar; bibliography)

“Neural Scene Representation and Rendering”, Eslami Et Al 2018

“Neural scene representation and rendering”⁠, S. M. Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S. Morcos, Marta Garnelo, Avraham Ruderman2018-06-15 (AI scaling; similar)

“Mining Gold from Implicit Models to Improve Likelihood-free Inference”, Brehmer Et Al 2018

“Mining gold from implicit models to improve likelihood-free inference”⁠, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer2018-05-30 (Bayes; backlinks; similar)

“Deep Reinforcement Learning in a Handful of Trials Using Probabilistic Dynamics Models”, Kurtl Et Al 2018

“Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models”⁠, Kurtl, Chua, Roberto Calandra, Rowan McAllister, Sergey Levine2018-05-30 (similar)

“Learning to Optimize Tensor Programs”, Chen Et Al 2018

“Learning to Optimize Tensor Programs”⁠, Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy2018-05-21 (CS/algorithm; backlinks; similar)

“Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks With Existing Applications”, Hadash Et Al 2018

“Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications”⁠, Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi, George Kou, Alon Jacovi2018-04-24 (active learning; similar)

“World Models”, Ha & Schmidhuber2018

“World Models”⁠, David Ha, Jürgen Schmidhuber2018-03-27 (similar)

“Differentiable Dynamic Programming for Structured Prediction and Attention”, Mensch & Blondel2018

“Differentiable Dynamic Programming for Structured Prediction and Attention”⁠, Arthur Mensch, Mathieu Blondel2018-02-11 (neural net⁠, decision theory; backlinks; similar)

“Generalization Guides Human Exploration in Vast Decision Spaces”, Wu Et Al 2018

“Planning Chemical Syntheses With Deep Neural Networks and Symbolic AI”, Segler Et Al 2018

“How to Explore Chemical Space Using Algorithms and Automation”, Gromski Et Al 2018

“Safe Policy Search With Gaussian Process Models”, Polymenakos Et Al 2017

“Safe Policy Search with Gaussian Process Models”⁠, Kyriakos Polymenakos, Alessandro Abate, Stephen Roberts2017-12-15 (similar)

“Analogical-based Bayesian Optimization”, Le Et Al 2017

“Analogical-based Bayesian Optimization”⁠, Trung Le, Khanh Nguyen, Tu Dinh Nguyen, Dinh Phung2017-09-19 (Bayes⁠, order statistics/comparison; backlinks; similar)

“A Game-Theoretic Analysis of the Off-Switch Game”, Wängberg Et Al 2017

“A Game-Theoretic Analysis of the Off-Switch Game”⁠, Tobias Wängberg, Mikael Böörs, Elliot Catt, Tom Everitt, Marcus Hutter2017-08-13 (similar)

“Neural Network Dynamics for Model-Based Deep Reinforcement Learning With Model-Free Fine-Tuning”, Nagabandi Et Al 2017

“Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning”⁠, Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing, Sergey Levine2017-08-08 (similar)

“Learning Transferable Architectures for Scalable Image Recognition”, Zoph Et Al 2017

“Learning Transferable Architectures for Scalable Image Recognition”⁠, Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le2017-07-21 (CNN; similar)

“Learning Model-based Planning from Scratch”, Pascanu Et Al 2017

“Learning model-based planning from scratch”⁠, Razvan Pascanu, Yujia Li, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sebastien Racanière, David Reichert2017-07-19 (similar)

“Value Prediction Network”, Oh Et Al 2017

“Value Prediction Network”⁠, Junhyuk Oh, Satinder Singh, Honglak Lee2017-07-11 (similar)

“Path Integral Networks: End-to-End Differentiable Optimal Control”, Okada Et Al 2017

“Path Integral Networks: End-to-End Differentiable Optimal Control”⁠, Masashi Okada, Luca Rigazio, Takenobu Aoshima2017-06-29 (similar)

“Visual Semantic Planning Using Deep Successor Representations”, Zhu Et Al 2017

“Visual Semantic Planning using Deep Successor Representations”⁠, Yuke Zhu, Daniel Gordon, Eric Kolve, Dieter Fox, Li Fei-Fei, Abhinav Gupta, Roozbeh Mottaghi, Ali Farhadi2017-05-23 (similar)

“AIXIjs: A Software Demo for General Reinforcement Learning”, Aslanides2017

“AIXIjs: A Software Demo for General Reinforcement Learning”⁠, John Aslanides2017-05-22 (JS; backlinks; similar)

“DeepArchitect: Automatically Designing and Training Deep Architectures”, Negrinho & Gordon2017

“DeepArchitect: Automatically Designing and Training Deep Architectures”⁠, Renato Negrinho, Geoff Gordon2017-04-28 (backlinks; similar)

“Stochastic Constraint Programming As Reinforcement Learning”, Prestwich Et Al 2017

“Stochastic Constraint Programming as Reinforcement Learning”⁠, Steven Prestwich, Roberto Rossi, Armagan Tarim2017-04-24 (CS/algorithm; backlinks; similar)

“Recurrent Environment Simulators”, Chiappa Et Al 2017

“Recurrent Environment Simulators”⁠, Silvia Chiappa, Sébastien Racaniere, Daan Wierstra, Shakir Mohamed2017-04-07 (RNN⁠, RL exploration; similar)

“Prediction and Control With Temporal Segment Models”, Mishra Et Al 2017

“Prediction and Control with Temporal Segment Models”⁠, Nikhil Mishra, Pieter Abbeel, Igor Mordatch2017-03-12 (CNN⁠, autoencoder NN; 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-free RL⁠, Bayes⁠, decision theory; backlinks; similar; bibliography)

“The Hippocampus As a Predictive Map”, Stachenfeld Et Al 2017

“The Predictron: End-To-End Learning and Planning”, Silver Et Al 2016

“The Predictron: End-To-End Learning and Planning”⁠, David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold2016-12-28 (similar)

“Model-based Adversarial Imitation Learning”, Baram Et Al 2016

“Model-based Adversarial Imitation Learning”⁠, Nir Baram, Oron Anschel, Shie Mannor2016-12-07 (similar)

“DeepMath—Deep Sequence Models for Premise Selection”, Alemi Et Al 2016

“DeepMath—Deep Sequence Models for Premise Selection”⁠, Alex A. Alemi, Francois Chollet, Niklas Een, Geoffrey Irving, Christian Szegedy, Josef Urban2016-06-14 (math)

“Value Iteration Networks”, Tamar Et Al 2016

“Value Iteration Networks”⁠, Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel2016-02-09 (CNN; similar)

“Resorting Media Ratings”, Branwen2015

“Resorting Media Ratings”⁠, Gwern Branwen2015-09-07 (sorting⁠, Bayes⁠, order statistics/comparison; backlinks; similar; bibliography)

“Compress and Control”, Veness Et Al 2014

“Compress and Control”⁠, Joel Veness, Marc G. Bellemare, Marcus Hutter, Alvin Chua, Guillaume Desjardins2014-11-19 (similar)

“Learning to Win by Reading Manuals in a Monte-Carlo Framework”, Branavan Et Al 2014

“Learning to Win by Reading Manuals in a Monte-Carlo Framework”⁠, S. R. K. Branavan, David Silver, Regina Barzilay2014-01-18 (retrieval AI; similar)

“Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science”, Clark2013

“Whatever next? Predictive brains, situated agents, and the future of cognitive science”⁠, Andy Clark2013-06-01 (neural net⁠, neuroscience; backlinks; similar)

“PUCT: Continuous Upper Confidence Trees With Polynomial Exploration-Consistency”, Auger Et Al 2013

“PUCT: Continuous Upper Confidence Trees with Polynomial Exploration-Consistency”⁠, David Auger, Adrien Cou?toux, Olivier Teytaud2013 (RL exploration; backlinks; similar)

“Planning As Satisfiability: Heuristics”, Rintanen2012

“Planning as satisfiability: Heuristics”⁠, Jussi Rintanen2012-12 (AI; backlinks; similar)

“A Monte Carlo AIXI Approximation”, Veness Et Al 2009

“A Monte Carlo AIXI Approximation”⁠, Joel Veness, Kee Siong Ng, Marcus Hutter, William Uther, David Silver2009-09-04 (AI; similar)

“Policy Mining: Learning Decision Policies from Fixed Sets of Data”, Zadrozny2003

“Policy Mining: Learning Decision Policies from Fixed Sets of Data”⁠, Bianca Zadrozny2003 (decision theory; similar)

“The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions”, Schmidhuber2002

“A Critique of Pure Reason”, McDermott1987

“A critique of pure reason”⁠, Drew McDermott1987-02-01 (AI⁠, philosophy/epistemology; similar)

“Human Window on the World”, Michie1985

Wikipedia

1. Algorithmic information theory