‘imitation learning’ tag

Gwern Branwen

‘imitation learning’ tag

See Also
Gwern
- “GPT-3 Creative Fiction”, Gwern 2020
- “The Scaling Hypothesis”, Gwern 2020
Links
Miscellaneous
Bibliography

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Gwern

“GPT-3 Creative Fiction”, Gwern 2020

GPT-3 Creative Fiction

“The Scaling Hypothesis”, Gwern 2020

The Scaling Hypothesis

Links

“A Revolution in How Robots Learn”

A Revolution in How Robots Learn

“Data Scaling Laws in Imitation Learning for Robotic Manipulation”, Lin et al 2024

Data Scaling Laws in Imitation Learning for Robotic Manipulation

“Motor Physics: Safety Implications of Geared Motors”, Jang 2024

Motor Physics: Safety implications of geared motors

“GUI-WORLD: A Dataset for GUI-Oriented Multimodal LLM-Based Agents”, Chen et al 2024

GUI-WORLD: A Dataset for GUI-oriented Multimodal LLM-based Agents

“Earnings Call: Tesla Discusses Q1 2024 Challenges and AI Expansion”, Abdulkadir 2024

Earnings call: Tesla Discusses Q1 2024 Challenges and AI Expansion

“Beyond A^✱: Better Planning With Transformers via Search Dynamics Bootstrapping (Searchformer)”, Lehnert et al 2024

Beyond A^✱: Better Planning with Transformers via Search Dynamics Bootstrapping (Searchformer)

“Grandmaster-Level Chess Without Search”, Ruoss et al 2024

Grandmaster-Level Chess Without Search

“Mobile ALOHA: Learning Bimanual Mobile Manipulation With Low-Cost Whole-Body Teleoperation”, Fu et al 2024

Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

“Vision-Language Models As a Source of Rewards”, Baumli et al 2023

Vision-Language Models as a Source of Rewards

“Self-Supervised Behavior Cloned Transformers Are Path Crawlers for Text Games”, Wang & Jansen 2023

Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Games

“Learning Few-Shot Imitation As Cultural Transmission”, Bhoopchand et al 2023

Learning few-shot imitation as cultural transmission

“Calibrated Language Models Must Hallucinate”, Kalai & Vempala 2023

Calibrated Language Models Must Hallucinate

“Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero”, Schut et al 2023

Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero

“Beyond Memorization: Violating Privacy Via Inference With Large Language Models”, Staab et al 2023

Beyond Memorization: Violating Privacy Via Inference with Large Language Models

“ReST: Reinforced Self-Training (ReST) for Language Modeling”, Gulcehre et al 2023

ReST: Reinforced Self-Training (ReST) for Language Modeling

“AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning”, Mathieu et al 2023

AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning

“Getting from Generative AI to Trustworthy AI: What LLMs Might Learn from Cyc”, Lenat & Marcus 2023

Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc

“Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior”, Block et al 2023

Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior

“Android in the Wild: A Large-Scale Dataset for Android Device Control”, Rawles et al 2023

Android in the Wild: A Large-Scale Dataset for Android Device Control

“GKD: Generalized Knowledge Distillation for Auto-Regressive Sequence Models”, Agarwal et al 2023

GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models

“ChessGPT: Bridging Policy Learning and Language Modeling”, Feng et al 2023

ChessGPT: Bridging Policy Learning and Language Modeling

“SequenceMatch: Imitation Learning for Autoregressive Sequence Modeling With Backtracking”, Cundy & Ermon 2023

SequenceMatch: Imitation Learning for Autoregressive Sequence Modeling with Backtracking

“Survival Instinct in Offline Reinforcement Learning”, Li et al 2023

Survival Instinct in Offline Reinforcement Learning

“Thought Cloning: Learning to Think While Acting by Imitating Human Thinking”, Hu & Clune 2023

Thought Cloning: Learning to Think while Acting by Imitating Human Thinking

“Let’s Verify Step by Step”, Lightman et al 2023

Let’s Verify Step by Step

“The False Promise of Imitating Proprietary LLMs”, Gudibande et al 2023

The False Promise of Imitating Proprietary LLMs

“LIMA: Less Is More for Alignment”, Zhou et al 2023

LIMA: Less Is More for Alignment

“Revisiting the Minimalist Approach to Offline Reinforcement Learning”, Tarasov et al 2023

Revisiting the Minimalist Approach to Offline Reinforcement Learning

“ACT: Learning Fine-Grained Bimanual Manipulation With Low-Cost Hardware”, Zhao et al 2023

ACT: Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

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

MimicPlay: Long-Horizon Imitation Learning by Watching Human Play

“Toolformer: Language Models Can Teach Themselves to Use Tools”, Schick et al 2023

Toolformer: Language Models Can Teach Themselves to Use Tools

“Conditioning Predictive Models: Risks and Strategies”, Hubinger et al 2023

Conditioning Predictive Models: Risks and Strategies

“Imitating Human Behavior With Diffusion Models”, Pearce et al 2023

Imitating Human Behavior with Diffusion Models

“Solving Math Word Problems With Process & Outcome-Based Feedback”, Uesato et al 2022

Solving math word problems with process & outcome-based feedback

“CICERO: Human-Level Play in the Game of Diplomacy by Combining Language Models With Strategic Reasoning”, Bakhtin et al 2022

CICERO: Human-level play in the game of Diplomacy by combining language models with strategic reasoning

“Token Turing Machines”, Ryoo et al 2022

Token Turing Machines

“Dungeons and Data: A Large-Scale NetHack Dataset”, Hambro et al 2022

Dungeons and Data: A Large-Scale NetHack Dataset

“In-Context Reinforcement Learning With Algorithm Distillation”, Laskin et al 2022

In-context Reinforcement Learning with Algorithm Distillation

“Scaling Laws for Reward Model Overoptimization”, Gao et al 2022

Scaling Laws for Reward Model Overoptimization

“Human-AI Coordination via Human-Regularized Search and Learning”, Hu et al 2022

Human-AI Coordination via Human-Regularized Search and Learning

“Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization”, Ramamurthy et al 2022

Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization

“Nearest Neighbor Non-Autoregressive Text Generation”, Niwa et al 2022

Nearest Neighbor Non-autoregressive Text Generation

“Generative Personas That Behave and Experience Like Humans”, Barthet et al 2022

Generative Personas That Behave and Experience Like Humans

“Diffusion-QL: Diffusion Policies As an Expressive Policy Class for Offline Reinforcement Learning”, Wang et al 2022

Diffusion-QL: Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning

“Limitations of Language Models in Arithmetic and Symbolic Induction”, Qian et al 2022

Limitations of Language Models in Arithmetic and Symbolic Induction

“Improved Policy Optimization for Online Imitation Learning”, Lavington et al 2022

Improved Policy Optimization for Online Imitation Learning

“Watch and Match: Supercharging Imitation With Regularized Optimal Transport”, Haldar et al 2022

Watch and Match: Supercharging Imitation with Regularized Optimal Transport

“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

“Large-Scale Retrieval for Reinforcement Learning”, Humphreys et al 2022

Large-Scale Retrieval for Reinforcement Learning

“Boosting Search Engines With Interactive Agents”, Ciaramita et al 2022

Boosting Search Engines with Interactive Agents

“Housekeep: Tidying Virtual Households Using Commonsense Reasoning”, Kant et al 2022

Housekeep: Tidying Virtual Households using Commonsense Reasoning

“When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?”, Kumar et al 2022

When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?

“Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale”, Ramrakhya et al 2022

Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale

“Imitating, Fast and Slow: Robust Learning from Demonstrations via Decision-Time Planning”, Qi et al 2022

Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning

“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

“Inferring Rewards from Language in Context”, Lin et al 2022

Inferring Rewards from Language in Context

“Robot Peels Banana With Goal-Conditioned Dual-Action Deep Imitation Learning”, Kim et al 2022

Robot peels banana with goal-conditioned dual-action deep imitation learning

“The Unsurprising Effectiveness of Pre-Trained Vision Models for Control”, Parisi et al 2022

The Unsurprising Effectiveness of Pre-Trained Vision Models for Control

“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

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

LID: Pre-Trained Language Models for Interactive Decision-Making

“Conditional Imitation Learning for Multi-Agent Games”, Shih et al 2022

Conditional Imitation Learning for Multi-Agent Games

“Amortized Noisy Channel Neural Machine Translation”, Pang et al 2021

Amortized Noisy Channel Neural Machine Translation

“WebGPT: Browser-Assisted Question-Answering With Human Feedback”, Nakano et al 2021

WebGPT: Browser-assisted question-answering with human feedback

“Modeling Strong and Human-Like Gameplay With KL-Regularized Search”, Jacob et al 2021

Modeling Strong and Human-Like Gameplay with KL-Regularized Search

“JueWu-MC: Playing Minecraft With Sample-Efficient Hierarchical Reinforcement Learning”, Lin et al 2021

JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning

“A General Language Assistant As a Laboratory for Alignment”, Askell et al 2021

A General Language Assistant as a Laboratory for Alignment

“AW-Opt: Learning Robotic Skills With Imitation and Reinforcement at Scale”, Lu et al 2021

AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale

“RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning”, Ramos et al 2021

RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning

“BC-Z: Zero-Shot Task Generalization With Robotic Imitation Learning”, Jang et al 2021

BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning

“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

“SafetyNet: Safe Planning for Real-World Self-Driving Vehicles Using Machine-Learned Policies”, Vitelli et al 2021

SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies

“TrufLL: Learning Natural Language Generation from Scratch”, Donati et al 2021

TrufLL: Learning Natural Language Generation from Scratch

“Relating Neural Text Degeneration to Exposure Bias”, Chiang & Chen 2021

Relating Neural Text Degeneration to Exposure Bias

“Learning to Navigate Sidewalks in Outdoor Environments”, Sorokin et al 2021

Learning to Navigate Sidewalks in Outdoor Environments

“PlaTe: Visually-Grounded Planning With Transformers in Procedural Tasks”, Sun et al 2021

PlaTe: Visually-Grounded Planning with Transformers in Procedural Tasks

“Implicit Behavioral Cloning”, Florence et al 2021

Implicit Behavioral Cloning

“DexMV: Imitation Learning for Dexterous Manipulation from Human Videos”, Qin et al 2021

DexMV: Imitation Learning for Dexterous Manipulation from Human Videos

“Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs”, Sonnerat et al 2021

Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs

“A Minimalist Approach to Offline Reinforcement Learning”, Fujimoto & Gu 2021

A Minimalist Approach to Offline Reinforcement Learning

“Hyperparameter Selection for Imitation Learning”, Hussenot et al 2021

Hyperparameter Selection for Imitation Learning

“From Motor Control to Team Play in Simulated Humanoid Football”, Liu et al 2021

From Motor Control to Team Play in Simulated Humanoid Football

“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

“Counter-Strike Deathmatch With Large-Scale Behavioral Cloning”, Pearce & Zhu 2021

Counter-Strike Deathmatch with Large-Scale Behavioral Cloning

“Fully General Online Imitation Learning”, Cohen et al 2021

Fully General Online Imitation Learning

“The MineRL 2020 Competition on Sample Efficient Reinforcement Learning Using Human Priors”, Guss et al 2021

The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors

“Meta Learning Backpropagation And Improving It”, Kirsch & Schmidhuber 2020

Meta Learning Backpropagation And Improving It

“SCC: an Efficient Deep Reinforcement Learning Agent Mastering the Game of StarCraft II”, Wang et al 2020

SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II

“Imitating Interactive Intelligence”, Abramson et al 2020

Imitating Interactive Intelligence

“TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game”, Han et al 2020

TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game

“RetinaGAN: An Object-Aware Approach to Sim-To-Real Transfer”, Ho et al 2020

RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer

“Emergent Social Learning via Multi-Agent Reinforcement Learning”, Ndousse et al 2020

Emergent Social Learning via Multi-agent Reinforcement Learning

“The NetHack Learning Environment”, Küttler et al 2020

The NetHack Learning Environment

“Automatic Discovery of Interpretable Planning Strategies”, Skirzyński et al 2020

Automatic Discovery of Interpretable Planning Strategies

“Learning Agile Robotic Locomotion Skills by Imitating Animals”, Peng et al 2020

Learning Agile Robotic Locomotion Skills by Imitating Animals

“Reinforcement Learning for Combinatorial Optimization: A Survey”, Mazyavkina et al 2020

Reinforcement Learning for Combinatorial Optimization: A Survey

“Bayesian REX: Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences”, Brown et al 2020

Bayesian REX: Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences

“AI Helps Warehouse Robots Pick Up New Tricks: Backed by Machine Learning Luminaries, Covariant.ai’s Bots Can Handle Jobs Previously Needing a Human Touch”, Knight 2020

AI Helps Warehouse Robots Pick Up New Tricks: Backed by machine learning luminaries, Covariant.ai’s bots can handle jobs previously needing a human touch

“Deep Bayesian Reward Learning from Preferences”, Brown & Niekum 2019

Deep Bayesian Reward Learning from Preferences

“Learning Norms from Stories: A Prior for Value Aligned Agents”, Frazier et al 2019

Learning Norms from Stories: A Prior for Value Aligned Agents

“Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?”, Du et al 2019

Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?

“Learning to Reason in Large Theories without Imitation”, Bansal et al 2019

Learning to Reason in Large Theories without Imitation

“The MineRL 2019 Competition on Sample Efficient Reinforcement Learning Using Human Priors”, Guss et al 2019

The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors

“Go-Explore: a New Approach for Hard-Exploration Problems”, Ecoffet et al 2019

Go-Explore: a New Approach for Hard-Exploration Problems

“Hierarchical Reinforcement Learning for Multi-Agent MOBA Game”, Zhang et al 2019

Hierarchical Reinforcement Learning for Multi-agent MOBA Game

“ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst”, Bansal et al 2018

ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst

“Reward Learning from Human Preferences and Demonstrations in Atari”, Ibarz et al 2018

Reward learning from human preferences and demonstrations in Atari

“Language GANs Falling Short”, Caccia et al 2018

Language GANs Falling Short

“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

“Human-Like Playtesting With Deep Learning”, Gudmundsson et al 2018

Human-Like Playtesting with Deep Learning

“Convergence of Value Aggregation for Imitation Learning”, Cheng & Boots 2018

Convergence of Value Aggregation for Imitation Learning

“Policy Optimization by Genetic Distillation”, Gangwani & Peng 2017

Policy Optimization by Genetic Distillation

“Learning to Play Chess With Minimal Lookahead and Deep Value Neural Networks”, Sabatelli 2017 (page 3)

Learning to Play Chess with Minimal Lookahead and Deep Value Neural Networks

“DropoutDAgger: A Bayesian Approach to Safe Imitation Learning”, Menda et al 2017

DropoutDAgger: A Bayesian Approach to Safe Imitation Learning

“One-Shot Visual Imitation Learning via Meta-Learning”, Finn et al 2017

One-Shot Visual Imitation Learning via Meta-Learning

“Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration”, Rahmatizadeh et al 2017

Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration

“Learning Human Behaviors from Motion Capture by Adversarial Imitation”, Merel et al 2017

Learning human behaviors from motion capture by adversarial imitation

“Grammatical Error Correction With Neural Reinforcement Learning”, Sakaguchi et al 2017

Grammatical Error Correction with Neural Reinforcement Learning

“Path Integral Networks: End-To-End Differentiable Optimal Control”, Okada et al 2017

Path Integral Networks: End-to-End Differentiable Optimal Control

“Gated-Attention Architectures for Task-Oriented Language Grounding”, Chaplot et al 2017

Gated-Attention Architectures for Task-Oriented Language Grounding

“Visual Semantic Planning Using Deep Successor Representations”, Zhu et al 2017

Visual Semantic Planning using Deep Successor Representations

“A Deep Reinforced Model for Abstractive Summarization”, Paulus et al 2017

A Deep Reinforced Model for Abstractive Summarization

“One-Shot Imitation Learning”, Duan et al 2017

One-Shot Imitation Learning

“Model-Based Adversarial Imitation Learning”, Baram et al 2016

Model-based Adversarial Imitation Learning

“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

“SeqGAN: Sequence Generative Adversarial Nets With Policy Gradient”, Yu et al 2016

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

“Generative Adversarial Imitation Learning”, Ho & Ermon 2016

Generative Adversarial Imitation Learning

“Mastering the Game of Go With Deep Neural Networks and Tree Search”, Silver et al 2016

Mastering the game of Go with deep neural networks and tree search

“An Invitation to Imitation”, Bagnell 2015

An Invitation to Imitation

“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

“The Hidden Structure of Overimitation”, Lyons et al 2007

The hidden structure of overimitation

“Google DeepMind’s Grandmaster-Level Chess Without Search”

Google DeepMind’s Grandmaster-Level Chess Without Search

“Sony’s Racing Car AI Just Destroyed Its Human Competitors—By Being Nice (and Fast)”

Sony’s racing car AI just destroyed its human competitors—by being nice (and fast)⁠:

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Miscellaneous

Bibliography

https://arxiv.org/abs/2402.04494#deepmind: “Grandmaster-Level Chess Without Search”, Anian Ruoss, Grégoire Delétang, Sourabh Medapati, Jordi Grau-Moya, Li Kevin Wenliang, Elliot Catt, John Reid, Tim Genewein

link-bibliography
https://www.nature.com/articles/s41467-023-42875-2#deepmind: “Learning Few-Shot Imitation As Cultural Transmission”, Avishkar Bhoopchand, Bethanie Brownfield, Adrian Collister, Agustin Dal Lago, Ashley Edwards, Richard Everett, Alexandre Fréchette, Yanko Gitahy Oliveira, Edward Hughes, Kory W. Mathewson, Piermaria Mendolicchio, Julia Pawar, Miruna Pȋslar, Alex Platonov, Evan Senter, Sukhdeep Singh, Alexander Zacherl, Lei M. Zhang

link-bibliography
https://arxiv.org/abs/2310.16410#deepmind: “Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero”, Lisa Schut, Nenad Tomasev, Tom McGrath, Demis Hassabis, Ulrich Paquet, Been Kim

link-bibliography
https://arxiv.org/abs/2308.04445: “Getting from Generative AI to Trustworthy AI: What LLMs Might Learn from Cyc”, Doug Lenat, Gary Marcus

link-bibliography
https://arxiv.org/abs/2306.05426: “SequenceMatch: Imitation Learning for Autoregressive Sequence Modeling With Backtracking”, Chris Cundy, Stefano Ermon

link-bibliography
https://arxiv.org/abs/2306.00323: “Thought Cloning: Learning to Think While Acting by Imitating Human Thinking”, Shengran Hu, Jeff Clune

link-bibliography
https://arxiv.org/abs/2305.20050#openai: “Let’s Verify Step by Step”, Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe

link-bibliography
https://arxiv.org/abs/2305.15717: “The False Promise of Imitating Proprietary LLMs”, Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song

link-bibliography
https://arxiv.org/abs/2305.09836: “Revisiting the Minimalist Approach to Offline Reinforcement Learning”, Denis Tarasov, Vladislav Kurenkov, Alexander Nikulin, Sergey Kolesnikov

link-bibliography
https://arxiv.org/abs/2304.13705: “ACT: Learning Fine-Grained Bimanual Manipulation With Low-Cost Hardware”, Tony Z. Zhao, Vikash Kumar, Sergey Levine, Chelsea Finn

link-bibliography
2022-bakhtin.pdf: “CICERO: Human-Level Play in the Game of Diplomacy by Combining Language Models With Strategic Reasoning”, Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, Andrew Goff, Jonathan Gray, Hengyuan Hu, Athul Paul Jacob, Mojtaba Komeili, Karthik Konath, Minae Kwon, Adam Lerer, Mike Lewis, Alexander H. Miller, Sasha Mitts, Adithya Renduchintala, Stephen Roller, Dirk Rowe, Weiyan Shi, Joe Spisak, Alexander Wei, David Wu, Hugh Zhang, Markus Zijlstra

link-bibliography
https://arxiv.org/abs/2210.10760#openai: “Scaling Laws for Reward Model Overoptimization”, Leo Gao, John Schulman, Jacob Hilton

link-bibliography
https://arxiv.org/abs/2210.01241: “Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization”, Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley, Jack Hessel, Rafet Sifa, Christian Bauckhage, Hannaneh Hajishirzi, Yejin Choi

link-bibliography
https://arxiv.org/abs/2206.11795#openai: “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 Sampedro, Jeff Clune

link-bibliography
https://arxiv.org/abs/2206.05314#deepmind: “Large-Scale Retrieval for Reinforcement Learning”, Peter C. Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Théophane Weber, Timothy Lillicrap

link-bibliography
https://openreview.net/forum?id=0ZbPmmB61g#google: “Boosting Search Engines With Interactive Agents”, Massimiliano Ciaramita, Leonard Adolphs, Michelle Chen Huebscher, Sascha Rothe, Christian Buck, Thomas Hofmann, Yannic Kilcher, Lasse Espeholt, Pier Giuseppe Sessa, Lierni Sestorain, Benjamin Börschinger

link-bibliography
https://arxiv.org/abs/2204.03514#facebook: “Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale”, Ram Ramrakhya, Eric Undersander, Dhruv Batra, Abhishek Das

link-bibliography
https://arxiv.org/abs/2112.09332#openai: “WebGPT: Browser-Assisted Question-Answering With Human Feedback”, Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, John Schulman

link-bibliography
https://arxiv.org/abs/2112.00861#anthropic: “A General Language Assistant As a Laboratory for Alignment”, Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy L. Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Jackson Kernion, Kamal Ndousse, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Jared Kaplan

link-bibliography
https://arxiv.org/abs/2105.12196#deepmind: “From Motor Control to Team Play in Simulated Humanoid Football”, Siqi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls, Thore Graepel, Nicolas Heess

link-bibliography
https://arxiv.org/abs/2101.11071: “The MineRL 2020 Competition on Sample Efficient Reinforcement Learning Using Human Priors”, William H. Guss, Mario Ynocente Castro, Sam Devlin, Brandon Houghton, Noboru Sean Kuno, Crissman Loomis, Stephanie Milani, Sharada Mohanty, Keisuke Nakata, Ruslan Salakhutdinov, John Schulman, Shinya Shiroshita, Nicholay Topin, Avinash Ummadisingu, Oriol Vinyals

link-bibliography
https://arxiv.org/abs/2012.05672#deepmind: “Imitating Interactive Intelligence”, 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 Lillicrap, Kory Mathewson, Alistair Muldal, Adam Santoro, Nikolay Savinov, Vikrant Varma, Greg Wayne, Nathaniel Wong, Chen Yan, Rui Zhu

link-bibliography
https://arxiv.org/abs/2011.13729#tencent: “TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game”, Lei Han, Jiechao Xiong, Peng Sun, Xinghai Sun, Meng Fang, Qingwei Guo, Qiaobo Chen, Tengfei Shi, Hongsheng Yu, Zhengyou Zhang

link-bibliography
https://arxiv.org/abs/1811.02549: “Language GANs Falling Short”, Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, Laurent Charlin

link-bibliography
2018-gudmundsson.pdf: “Human-Like Playtesting With Deep Learning”, Stefan Freyr Gudmundsson, Philipp Eisen, Erik Poromaa, Alex Nodet, Sami Purmonen, Bartlomiej Kozakowski, Richard Meurling, Lele Cao

link-bibliography
2017-sabatelli.pdf#page=3: “Learning to Play Chess With Minimal Lookahead and Deep Value Neural Networks”, Matthia Sabatelli

link-bibliography