“‘RL Exploration’ Tag”,2019-09-06 (ML dataset, information theory, bipolar, depression, willpower, hidden-information game, AlphaGo, MARL, decision theory; backlinks)⁠:

Bibliography for tag reinforcement-learning/exploration, most recent first: 8 related tags, 312 annotations, & 40 links (parent).

• See Also
• Gwern
  • “Can You Unsort Lists for Diversity?”, Gwern2019
  • “Number Search Engine via NN Embeddings”, Gwern2024
  • “Novelty Nets: Classifier Anti-Guidance”, Gwern2024
  • “Free-Play Periods for RL Agents”, Gwern2023
  • “Candy Japan’s New Box A/B Test”, Gwern2016
• Links
  • “SimpleStrat: Diversifying Language Model Generation With Stratification”, Wong et al 2024
  • “Intelligent Go-Explore (IGE): Standing on the Shoulders of Giant Foundation Models”, Lu et al 2024
  • “Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts”, Samvelyan et al 2024
  • “Self-Supervised Behavior Cloned Transformers Are Path Crawlers for Text Games”, Wang & Jansen2023
  • “Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations”, Hong et al 2023
  • “QDAIF: Quality-Diversity through AI Feedback”, Bradley et al 2023
  • “Beyond Memorization: Violating Privacy Via Inference With Large Language Models”, Staab et al 2023
  • “Let Models Speak Ciphers: Multiagent Debate through Embeddings”, Pham et al 2023
  • “Small Batch Deep Reinforcement Learning”, Obando-Ceron et al 2023
  • “Language Reward Modulation for Pretraining Reinforcement Learning”, Adeniji et al 2023
  • “Diversifying AI: Towards Creative Chess With AlphaZero (AZ_db)”, Zahavy et al 2023
  • “Supervised Pretraining Can Learn In-Context Reinforcement Learning”, Lee et al 2023
  • “Learning to Generate Novel Scientific Directions With Contextualized Literature-Based Discovery”, Wang et al 2023
  • “You And Your Research”, Hamming2023
  • “Long-Term Value of Exploration: Measurements, Findings and Algorithms”, Su et al 2023
  • “Inducing Anxiety in GPT-3.5 Increases Exploration and Bias”, Coda-Forno et al 2023
  • “Reflexion: Language Agents With Verbal Reinforcement Learning”, Shinn et al 2023
  • “MimicPlay: Long-Horizon Imitation Learning by Watching Human Play”, Wang et al 2023
  • “MarioGPT: Open-Ended Text2Level Generation through Large Language Models”, Sudhakaran et al 2023
  • “DreamerV3: Mastering Diverse Domains through World Models”, Hafner et al 2023
  • “AlphaZe∗∗: AlphaZero-Like Baselines for Imperfect Information Games Are Surprisingly Strong”, Blüml et al 2023
  • “Effect of Lysergic Acid Diethylamide (LSD) on Reinforcement Learning in Humans”, Kanen et al 2022
  • “Curiosity in Hindsight”, Jarrett et al 2022
  • “In-Context Reinforcement Learning With Algorithm Distillation”, Laskin et al 2022
  • “E3B: Exploration via Elliptical Episodic Bonuses”, Henaff et al 2022
  • “Vote-K: Selective Annotation Makes Language Models Better Few-Shot Learners”, Su et al 2022
  • “LGE: Cell-Free Latent Go-Explore”, Gallouédec & Dellandréa2022
  • “A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning”, Dann et al 2022
  • “Trajectory Autoencoding Planner: Efficient Planning in a Compact Latent Action Space”, Jiang et al 2022
  • “Value-Free Random Exploration Is Linked to Impulsivity”, Dubois & Hauser2022
  • “Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling”, Nguyen & Grover2022
  • “The Cost of Information Acquisition by Natural Selection”, McGee et al 2022
  • “Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos”, Baker et al 2022
  • “BYOL-Explore: Exploration by Bootstrapped Prediction”, Guo et al 2022
  • “Multi-Objective Hyperparameter Optimization—An Overview”, Karl et al 2022
  • “Director: Deep Hierarchical Planning from Pixels”, Hafner et al 2022
  • “Boosting Search Engines With Interactive Agents”, Ciaramita et al 2022
  • “Towards Learning Universal Hyperparameter Optimizers With Transformers”, Chen et al 2022
  • “Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments”, Sullivan et al 2022
  • “Effective Mutation Rate Adaptation through Group Elite Selection”, Kumar et al 2022
  • “Semantic Exploration from Language Abstractions and Pretrained Representations”, Tam et al 2022
  • “Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale”, Ramrakhya et al 2022
  • “CLIP on Wheels (CoW): Zero-Shot Object Navigation As Object Localization and Exploration”, Gadre et al 2022
  • “Policy Improvement by Planning With Gumbel”, Danihelka et al 2022
  • “Evolving Curricula With Regret-Based Environment Design”, Parker-Holder et al 2022
  • “VAPO: Affordance Learning from Play for Sample-Efficient Policy Learning”, Borja-Diaz et al 2022
  • “Learning Causal Overhypotheses through Exploration in Children and Computational Models”, Kosoy et al 2022
  • “Policy Learning and Evaluation With Randomized Quasi-Monte Carlo”, Arnold et al 2022
  • “NeuPL: Neural Population Learning”, Liu et al 2022
  • “ODT: Online Decision Transformer”, Zheng et al 2022
  • “EvoJAX: Hardware-Accelerated Neuroevolution”, Tang et al 2022
  • “LID: Pre-Trained Language Models for Interactive Decision-Making”, Li et al 2022
  • “Accelerated Quality-Diversity for Robotics through Massive Parallelism”, Lim et al 2022
  • “Rotting Infinitely Many-Armed Bandits”, Kim et al 2022
  • “Don’t Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning (ExORL)”, Yarats et al 2022
  • “Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination”, Lucas & Allen2022
  • “Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots”, Bhatia et al 2022
  • “Environment Generation for Zero-Shot Compositional Reinforcement Learning”, Gur et al 2022
  • “Safe Deep RL in 3D Environments Using Human Feedback”, Rahtz et al 2022
  • “Automated Reinforcement Learning (AutoRL): A Survey and Open Problems”, Parker-Holder et al 2022
  • “Maximum Entropy Population Based Training for Zero-Shot Human-AI Coordination”, Zhao et al 2021
  • “The Costs and Benefits of Dispersal in Small Populations”, Polechova2021
  • “The Geometry of Decision-Making in Individuals and Collectives”, Sridhar et al 2021
  • “An Experimental Design Perspective on Model-Based Reinforcement Learning”, Mehta et al 2021
  • “JueWu-MC: Playing Minecraft With Sample-Efficient Hierarchical Reinforcement Learning”, Lin et al 2021
  • “Procedural Generalization by Planning With Self-Supervised World Models”, Anand et al 2021
  • “Correspondence between Neuroevolution and Gradient Descent”, Whitelam et al 2021
  • “URLB: Unsupervised Reinforcement Learning Benchmark”, Laskin et al 2021
  • “Mastering Atari Games With Limited Data”, Ye et al 2021
  • “Discovering and Achieving Goals via World Models”, Mendonca et al 2021
  • “The Structure of Genotype-Phenotype Maps Makes Fitness Landscapes Navigable”, Greenbury et al 2021
  • “Replay-Guided Adversarial Environment Design”, Jiang et al 2021
  • “A Review of the Gumbel-Max Trick and Its Extensions for Discrete Stochasticity in Machine Learning”, Huijben et al 2021
  • “Monkey Plays Pac-Man With Compositional Strategies and Hierarchical Decision-Making”, Yang et al 2021
  • “Neural Autopilot and Context-Sensitivity of Habits”, Camerer & Li2021
  • “Algorithmic Balancing of Familiarity, Similarity, & Discovery in Music Recommendations”, Mehrotra2021
  • “TrufLL: Learning Natural Language Generation from Scratch”, Donati et al 2021
  • “Is Curiosity All You Need? On the Utility of Emergent Behaviors from Curious Exploration”, Groth et al 2021
  • “Bootstrapped Meta-Learning”, Flennerhag et al 2021
  • “Open-Ended Learning Leads to Generally Capable Agents”, Team et al 2021
  • “Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs”, Sonnerat et al 2021
  • “Why Generalization in RL Is Difficult: Epistemic POMDPs and Implicit Partial Observability”, Ghosh et al 2021
  • “Imitation-Driven Cultural Collapse”, Duran-Nebreda & Valverde2021
  • “Multi-Task Curriculum Learning in a Complex, Visual, Hard-Exploration Domain: Minecraft”, Kanitscheider et al 2021
  • “Learning to Hesitate”, Descamps et al 2021
  • “Planning for Novelty: Width-Based Algorithms for Common Problems in Control, Planning and Reinforcement Learning”, Lipovetzky2021
  • “Trajectory Transformer: Reinforcement Learning As One Big Sequence Modeling Problem”, Janner et al 2021
  • “From Motor Control to Team Play in Simulated Humanoid Football”, Liu et al 2021
  • “Reward Is Enough”, Silver et al 2021
  • “Principled Exploration via Optimistic Bootstrapping and Backward Induction”, Bai et al 2021
  • “Intelligence and Unambitiousness Using Algorithmic Information Theory”, Cohen et al 2021
  • “Deep Bandits Show-Off: Simple and Efficient Exploration With Deep Networks”, Zhu & Rigotti2021
  • “On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning”, Vischer et al 2021
  • “What Are Bayesian Neural Network Posteriors Really Like?”, Izmailov et al 2021
  • “Epistemic Autonomy: Self-Supervised Learning in the Mammalian Hippocampus”, Santos-Pata et al 2021
  • “Bayesian Optimization Is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020”, Turner et al 2021
  • “Flexible Modulation of Sequence Generation in the Entorhinal-Hippocampal System”, McNamee et al 2021
  • “Reinforcement Learning, Bit by Bit”, Lu et al 2021
  • “Asymmetric Self-Play for Automatic Goal Discovery in Robotic Manipulation”, OpenAI et al 2021
  • “Informational Herding, Optimal Experimentation, and Contrarianism”, Smith et al 2021
  • “Go-Explore: First Return, Then Explore”, Ecoffet et al 2021
  • “TacticZero: Learning to Prove Theorems from Scratch With Deep Reinforcement Learning”, Wu et al 2021
  • “Proof Artifact Co-Training for Theorem Proving With Language Models”, Han et al 2021
  • “The MineRL 2020 Competition on Sample Efficient Reinforcement Learning Using Human Priors”, Guss et al 2021
  • “Curriculum Learning: A Survey”, Soviany et al 2021
  • “MAP-Elites Enables Powerful Stepping Stones and Diversity for Modular Robotics”, Nordmoen et al 2021
  • “Is Pessimism Provably Efficient for Offline RL?”, Jin et al 2020
  • “Monte-Carlo Graph Search for AlphaZero”, Czech et al 2020
  • “Imitating Interactive Intelligence”, Abramson et al 2020
  • “Emergent Complexity and Zero-Shot Transfer via Unsupervised Environment Design”, Dennis et al 2020
  • “Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian”, Parker-Holder et al 2020
  • “Meta-Trained Agents Implement Bayes-Optimal Agents”, Mikulik et al 2020
  • “Learning Not to Learn: Nature versus Nurture in Silico”, Lange & Sprekeler2020
  • “The Child As Hacker”, Rule et al 2020
  • “Assessing Game Balance With AlphaZero: Exploring Alternative Rule Sets in Chess”, Tomašev et al 2020
  • “The Temporal Dynamics of Opportunity Costs: A Normative Account of Cognitive Fatigue and Boredom”, Agrawal et al 2020
  • “The Overfitted Brain: Dreams Evolved to Assist Generalization”, Hoel2020
  • “Exploration Strategies in Deep Reinforcement Learning”, Weng2020
  • “Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search”, Rawal et al 2020
  • “Automatic Discovery of Interpretable Planning Strategies”, Skirzyński et al 2020
  • “IJON: Exploring Deep State Spaces via Fuzzing”, Aschermann et al 2020
  • “Planning to Explore via Self-Supervised World Models”, Sekar et al 2020
  • “Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems”, Levine et al 2020
  • “Pitfalls of Learning a Reward Function Online”, Armstrong et al 2020
  • “First Return, Then Explore”, Ecoffet et al 2020
  • “Real World Games Look Like Spinning Tops”, Czarnecki et al 2020
  • “Approximate Exploitability: Learning a Best Response in Large Games”, Timbers et al 2020
  • “Agent57: Outperforming the Human Atari Benchmark”, Puigdomènech et al 2020
  • “Agent57: Outperforming the Atari Human Benchmark”, Badia et al 2020
  • “Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and Their Solutions”, Wang et al 2020
  • “Meta-Learning Curiosity Algorithms”, Alet et al 2020
  • “Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey”, Narvekar et al 2020
  • “AutoML-Zero: Evolving Machine Learning Algorithms From Scratch”, Real et al 2020
  • “Never Give Up: Learning Directed Exploration Strategies”, Badia et al 2020
  • “Effective Diversity in Population Based Reinforcement Learning”, Parker-Holder et al 2020
  • “Near-Perfect Point-Goal Navigation from 2.5 Billion Frames of Experience”, Wijmans & Kadian2020
  • “MicrobatchGAN: Stimulating Diversity With Multi-Adversarial Discrimination”, Mordido et al 2020
  • “Learning Human Objectives by Evaluating Hypothetical Behavior”, Reddy et al 2019
  • “Optimal Policies Tend to Seek Power”, Turner et al 2019
  • “DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames”, Wijmans et al 2019
  • “Emergent Tool Use From Multi-Agent Autocurricula”, Baker et al 2019
  • “Emergent Tool Use from Multi-Agent Interaction § Surprising Behavior”, Baker et al 2019
  • “Emergent Tool Use from Multi-Agent Interaction § Surprising Behavior”, Baker et al 2019
  • “R2D3: Making Efficient Use of Demonstrations to Solve Hard Exploration Problems”, Paine et al 2019
  • “Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment”, Taïga et al 2019
  • “A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment”, Leibfried et al 2019
  • “An Optimistic Perspective on Offline Reinforcement Learning”, Agarwal et al 2019
  • “Meta Reinforcement Learning”, Weng2019
  • “Search on the Replay Buffer: Bridging Planning and Reinforcement Learning”, Eysenbach et al 2019
  • “ICML 2019 Notes”, Abel2019
  • “Human-Level Performance in 3D Multiplayer Games With Population-Based Reinforcement Learning”, Jaderberg et al 2019
  • “AI-GAs: AI-Generating Algorithms, an Alternate Paradigm for Producing General Artificial Intelligence”, Clune2019
  • “Learning to Reason in Large Theories without Imitation”, Bansal et al 2019
  • “Reinforcement Learning, Fast and Slow”, Botvinick et al 2019
  • “Meta Reinforcement Learning As Task Inference”, Humplik et al 2019
  • “Meta-Learning of Sequential Strategies”, Ortega et al 2019
  • “The MineRL 2019 Competition on Sample Efficient Reinforcement Learning Using Human Priors”, Guss et al 2019
  • “Π-IW: Deep Policies for Width-Based Planning in Pixel Domains”, Junyent et al 2019
  • “Learning To Follow Directions in Street View”, Hermann et al 2019
  • “A Generalized Framework for Population Based Training”, Li et al 2019
  • “Go-Explore: a New Approach for Hard-Exploration Problems”, Ecoffet et al 2019
  • “Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions”, Wang et al 2019
  • “Is the FDA Too Conservative or Too Aggressive?: A Bayesian Decision Analysis of Clinical Trial Design”, Isakov et al 2019
  • “V-Fuzz: Vulnerability-Oriented Evolutionary Fuzzing”, Li et al 2019
  • “Common Neural Code for Reward and Information Value”, Kobayashi & Hsu2019
  • “Machine-Learning-Guided Directed Evolution for Protein Engineering”, Yang et al 2019
  • “Enjoy It Again: Repeat Experiences Are Less Repetitive Than People Think”, O’Brien2019
  • “Evolutionary-Neural Hybrid Agents for Architecture Search”, Maziarz et al 2018
  • “The Bayesian Superorganism III: Externalized Memories Facilitate Distributed Sampling”, Hunt et al 2018
  • “Exploration in the Wild”, Schulz et al 2018
  • “Off-Policy Deep Reinforcement Learning without Exploration”, Fujimoto et al 2018
  • “An Introduction to Deep Reinforcement Learning”, Francois-Lavet et al 2018
  • “The Bayesian Superorganism I: Collective Probability Estimation”, Hunt et al 2018
  • “Exploration by Random Network Distillation”, Burda et al 2018
  • “Computational Noise in Reward-Guided Learning Drives Behavioral Variability in Volatile Environments”, Findling et al 2018
  • “RND: Large-Scale Study of Curiosity-Driven Learning”, Burda et al 2018
  • “Visual Reinforcement Learning With Imagined Goals”, Nair et al 2018
  • “Is Q-Learning Provably Efficient?”, Jin et al 2018
  • “Improving Width-Based Planning With Compact Policies”, Junyent et al 2018
  • “Construction of Arbitrarily Strong Amplifiers of Natural Selection Using Evolutionary Graph Theory”, Pavlogiannis et al 2018
  • “Re-Evaluating Evaluation”, Balduzzi et al 2018
  • “DVRL: Deep Variational Reinforcement Learning for POMDPs”, Igl et al 2018
  • “Mix&Match—Agent Curricula for Reinforcement Learning”, Czarnecki et al 2018
  • “Playing Hard Exploration Games by Watching YouTube”, Aytar et al 2018
  • “Observe and Look Further: Achieving Consistent Performance on Atari”, Pohlen et al 2018
  • “Generalization and Search in Risky Environments”, Schulz et al 2018
  • “Toward Diverse Text Generation With Inverse Reinforcement Learning”, Shi et al 2018
  • “Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution”, Elsken et al 2018
  • “Learning to Navigate in Cities Without a Map”, Mirowski et al 2018
  • “The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities”, Lehman et al 2018
  • “Some Considerations on Learning to Explore via Meta-Reinforcement Learning”, Stadie et al 2018
  • “Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling”, Riquelme et al 2018
  • “Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration”, Liu et al 2018
  • “Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari”, Chrabaszcz et al 2018
  • “One Big Net For Everything”, Schmidhuber2018
  • “Learning to Search With MCTSnets”, Guez et al 2018
  • “Learning and Querying Fast Generative Models for Reinforcement Learning”, Buesing et al 2018
  • “Safe Exploration in Continuous Action Spaces”, Dalal et al 2018
  • “Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning”, Anderson et al 2018
  • “Deep Reinforcement Fuzzing”, Böttinger et al 2018
  • “Planning Chemical Syntheses With Deep Neural Networks and Symbolic AI”, Segler et al 2018
  • “Generalization Guides Human Exploration in Vast Decision Spaces”, Wu et al 2018
  • “Innovation and Cumulative Culture through Tweaks and Leaps in Online Programming Contests”, Miu et al 2018
  • “A Flexible Approach to Automated RNN Architecture Generation”, Schrimpf et al 2017
  • “Finding Competitive Network Architectures Within a Day Using UCT”, Wistuba2017
  • “Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents”, Conti et al 2017
  • “Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning”, Such et al 2017
  • “The Paradoxical Sustainability of Periodic Migration and Habitat Destruction”, Tan & Cheong2017
  • “Posterior Sampling for Large Scale Reinforcement Learning”, Theocharous et al 2017
  • “Policy Optimization by Genetic Distillation”, Gangwani & Peng2017
  • “Emergent Complexity via Multi-Agent Competition”, Bansal et al 2017
  • “An Analysis of the Value of Information When Exploring Stochastic, Discrete Multi-Armed Bandits”, Sledge & Principe2017
  • “The Uncertainty Bellman Equation and Exploration”, O’Donoghue et al 2017
  • “Changing Their Tune: How Consumers’ Adoption of Online Streaming Affects Music Consumption and Discovery”, Datta et al 2017
  • “A Rational Choice Framework for Collective Behavior”, Krafft2017
  • “Imagination-Augmented Agents for Deep Reinforcement Learning”, Weber et al 2017
  • “Distral: Robust Multitask Reinforcement Learning”, Teh et al 2017
  • “The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously”, Cabi et al 2017
  • “Emergence of Locomotion Behaviors in Rich Environments”, Heess et al 2017
  • “Noisy Networks for Exploration”, Fortunato et al 2017
  • “CAN: Creative Adversarial Networks, Generating “Art” by Learning About Styles and Deviating from Style Norms”, Elgammal et al 2017
  • “Device Placement Optimization With Reinforcement Learning”, Mirhoseini et al 2017
  • “Towards Synthesizing Complex Programs from Input-Output Examples”, Chen et al 2017
  • “Scalable Generalized Linear Bandits: Online Computation and Hashing”, Jun et al 2017
  • “DeepXplore: Automated Whitebox Testing of Deep Learning Systems”, Pei et al 2017
  • “Recurrent Environment Simulators”, Chiappa et al 2017
  • “Learned Optimizers That Scale and Generalize”, Wichrowska et al 2017
  • “Evolution Strategies As a Scalable Alternative to Reinforcement Learning”, Salimans et al 2017
  • “Large-Scale Evolution of Image Classifiers”, Real et al 2017
  • “CoDeepNEAT: Evolving Deep Neural Networks”, Miikkulainen et al 2017
  • “Rotting Bandits”, Levine et al 2017
  • “Neural Combinatorial Optimization With Reinforcement Learning”, Bello et al 2017
  • “Neural Data Filter for Bootstrapping Stochastic Gradient Descent”, Fan et al 2017
  • “Search in Patchy Media: Exploitation-Exploration Tradeoff”
  • “Towards Information-Seeking Agents”, Bachman et al 2016
  • “Exploration and Exploitation of Victorian Science in Darwin’s Reading Notebooks”, Murdock et al 2016
  • “Learning to Learn without Gradient Descent by Gradient Descent”, Chen et al 2016
  • “Learning to Perform Physics Experiments via Deep Reinforcement Learning”, Denil et al 2016
  • “Neural Architecture Search With Reinforcement Learning”, Zoph & Le2016
  • “Combating Reinforcement Learning’s Sisyphean Curse With Intrinsic Fear”, Lipton et al 2016
  • “Bayesian Reinforcement Learning: A Survey”, Ghavamzadeh et al 2016
  • “Human Collective Intelligence As Distributed Bayesian Inference”, Krafft et al 2016
  • “Universal Darwinism As a Process of Bayesian Inference”, Campbell2016
  • “Unifying Count-Based Exploration and Intrinsic Motivation”, Bellemare et al 2016
  • “D-TS: Double Thompson Sampling for Dueling Bandits”, Wu & Liu2016
  • “Improving Information Extraction by Acquiring External Evidence With Reinforcement Learning”, Narasimhan et al 2016
  • “Deep Exploration via Bootstrapped DQN”, Osband et al 2016
  • “The Netflix Recommender System”, Gomez-Uribe & Hunt2015
  • “On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models”, Schmidhuber2015
  • “Online Batch Selection for Faster Training of Neural Networks”, Loshchilov & Hutter2015
  • “MAP-Elites: Illuminating Search Spaces by Mapping Elites”, Mouret & Clune2015
  • “What My Deep Model Doesn’t Know…”, Gal2015
  • “The Psychology and Neuroscience of Curiosity”, Kidd & Hayden2015
  • “Thompson Sampling With the Online Bootstrap”, Eckles & Kaptein2014
  • “On the Complexity of Best Arm Identification in Multi-Armed Bandit Models”, Kaufmann et al 2014
  • “Robots That Can Adapt like Animals”, Cully et al 2014
  • “Freeze-Thaw Bayesian Optimization”, Swersky et al 2014
  • “Search for the Wreckage of Air France Flight AF 447”, Stone et al 2014
  • “(More) Efficient Reinforcement Learning via Posterior Sampling”, Osband et al 2013
  • “Model-Based Bayesian Exploration”, Dearden et al 2013
  • “PUCT: Continuous Upper Confidence Trees With Polynomial Exploration-Consistency”, Auger et al 2013
  • “(More) Efficient Reinforcement Learning via Posterior Sampling [PSRL]”, Osband2013
  • “Experimental Design for Partially Observed Markov Decision Processes”, Thorbergsson & Hooker2012
  • “Learning Is Planning: near Bayes-Optimal Reinforcement Learning via Monte-Carlo Tree Search”, Asmuth & Littman2012
  • “PILCO: A Model-Based and Data-Efficient Approach to Policy Search”, Deisenroth & Rasmussen2011
  • “Abandoning Objectives: Evolution Through the Search for Novelty Alone”, Lehman & Stanley2011
  • “Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments”, Sun et al 2011
  • “Age-Fitness Pareto Optimization”, Schmidt & Lipson2010
  • “Monte-Carlo Planning in Large POMDPs”, Silver & Veness2010
  • “Formal Theory of Creativity & Fun & Intrinsic Motivation (1990^–₂₀2010_14ya)”, Schmidhuber2010
  • “The Epistemic Benefit of Transient Diversity”, Zollman2009
  • “Specialization Effect and Its Influence on Memory and Problem Solving in Expert Chess Players”, Bilalić et al 2009
  • “Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes”, Schmidhuber2008
  • “Pure Exploration for Multi-Armed Bandit Problems”, Bubeck et al 2008
  • “Exploiting Open-Endedness to Solve Problems Through the Search for Novelty”, Lehman & Stanley2008
  • “Towards Efficient Evolutionary Design of Autonomous Robots”, Krčah2008
  • “Resilient Machines Through Continuous Self-Modeling”, Bongard et al 2006
  • “ALPS: the Age-Layered Population Structure for Reducing the Problem of Premature Convergence”, Hornby2006
  • “Bayesian Adaptive Exploration”, Loredo & Chernoff2003
  • “NEAT: Evolving Neural Networks through Augmenting Topologies”, Stanley & Miikkulainen2002
  • “A Bayesian Framework for Reinforcement Learning”, Strens2000
  • “Case Studies in Evolutionary Experimentation and Computation”, Rechenberg2000
  • “Efficient Progressive Sampling”, Provost et al 1999b
  • “Evolving 3D Morphology and Behavior by Competition”, Sims1994
  • “Interactions between Learning and Evolution”, Ackley & Littman1992
  • “Evolution Strategy: Nature’s Way of Optimization”, Rechenberg1989
  • “The Analysis of Sequential Experiments With Feedback to Subjects”, Diaconis & Graham1981
  • “Evolutionsstrategien”, Rechenberg1977
  • Evolutionsstrategie: Optimierung Technischer Systeme Nach Prinzipien Der Biologischen Evolution, Rechenberg1973
  • “The Usefulness of Useless Knowledge”, Flexner1939
  • “Curiosity Killed the Mario”
  • “Brian Christian on Computer Science Algorithms That Tackle Fundamental and Universal Problems”
  • “Solving Zelda With the Antithesis SDK”
  • “Goodhart’s Law, Diversity and a Series of Seemingly Unrelated Toy Problems”
  • “Why Generalization in RL Is Difficult: Epistemic POMDPs and Implicit Partial Observability [Blog]”
  • Bayesian Optimization Book
  • “Temporal Difference Learning and TD-Gammon”
  • “An Experimental Design Perspective on Model-Based Reinforcement Learning [Blog]”
  • “Safety-First AI for Autonomous Data Center Cooling and Industrial Control”
  • “Pulling JPEGs out of Thin Air”
  • “Curriculum For Reinforcement Learning”
  • “Why Testing Self-Driving Cars in SF Is Challenging but Necessary”
  • “Reinforcement Learning With Prediction-Based Rewards”
  • “Prompting Diverse Ideas: Increasing AI Idea Variance”
  • “You Need a Novelty Budget”
  • “ChatGPT As Muse, Not Oracle”, Litt2024
  • “Conditions for Mathematical Equivalence of Stochastic Gradient Descent and Natural Selection”
  • “Probable Points and Credible Intervals, Part 2: Decision Theory”
  • “AI Is Learning How to Create Itself”
  • “Montezuma’s Revenge Solved by Go-Explore, a New Algorithm for Hard-Exploration Problems (Sets Records on Pitfall, Too)”
  • “Monkeys Play Pac-Man”
  • “Playing Montezuma’s Revenge With Intrinsic Motivation”
  • Sort By Magic
    • diverse-learning
    • sample-efficiency
    • goal-discovery
    • curiosity-driven
  • Wikipedia
• Miscellaneous
• Bibliography

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