“‘Model-Free RL’ Tag”,2018-12-12

Bibliography for tag reinforcement-learning/model-free, most recent first: 2 related tags, 288 annotations, & 25 links (parent).

• See Also
• Links
  • “Deep Reinforcement Learning Without Experience Replay, Target Networks, or Batch Updates”, Elsayed et al 2024
  • “Centaur: a Foundation Model of Human Cognition”, Binz et al 2024
  • “SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning”, Lee et al 2024
  • “Training Language Models to Self-Correct via Reinforcement Learning”, Kumar et al 2024
  • “Carpentopod: A Walking Table Project”, Carpentier2024
  • “Mind Wandering During Implicit Learning Is Associated With Increased Periodic EEG Activity And Improved Extraction Of Hidden Probabilistic Patterns”, Simor et al 2024
  • “Alexa Is in Millions of Households—And Amazon Is Losing Billions”, Mattioli2024
  • “Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo”, Zhao et al 2024
  • “Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data”, Tajwar et al 2024
  • “CodeIt: Self-Improving Language Models With Prioritized Hindsight Replay”, Butt et al 2024
  • “ReST Meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent”, Aksitov et al 2023
  • “Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic Reasoning”, Dutta et al 2023
  • “Let Models Speak Ciphers: Multiagent Debate through Embeddings”, Pham et al 2023
  • “Predictive Auxiliary Objectives in Deep RL Mimic Learning in the Brain”, Fang & Stachenfeld2023
  • “Small Batch Deep Reinforcement Learning”, Obando-Ceron et al 2023
  • “Subwords As Skills: Tokenization for Sparse-Reward Reinforcement Learning”, Yunis et al 2023
  • “Comparative Study of Model-Based and Model-Free Reinforcement Learning Control Performance in HVAC Systems”, Gao & Wang2023
  • “What Are Dreams For? Converging Lines of Research Suggest That We Might Be Misunderstanding Something We Do Every Night of Our Lives”, Gefter2023
  • “Learning to Model the World With Language”, Lin et al 2023
  • “Low-Poly Image Generation Using Evolutionary Algorithms in Ruby”
  • “Using Temperature to Analyze the Neural Basis of a Time-Based Decision”, Monteiro et al 2023
  • “Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks”, Chevalier-Boisvert et al 2023
  • “Twitching in Sensorimotor Development from Sleeping Rats to Robots”, Blumberg et al 2023
  • “Universal Mechanical Polycomputation in Granular Matter”, Parsa et al 2023
  • “Lucy-SKG: Learning to Play Rocket League Efficiently Using Deep Reinforcement Learning”, Moschopoulos et al 2023
  • “Improving Language Models With Advantage-Based Offline Policy Gradients”, Baheti et al 2023
  • “Reinforcement Learning in Newcomb-Like Environments”, Bell et al 2023
  • “WizardLM: Empowering Large Language Models to Follow Complex Instructions”, Xu et al 2023
  • “Bridging Discrete and Backpropagation: Straight-Through and Beyond”, Liu et al 2023
  • “Empirical Design in Reinforcement Learning”, Patterson et al 2023
  • “A Circuit Mechanism Linking past and Future Learning through Shifts in Perception”, Crossley et al 2023
  • “Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier”, D’Oro et al 2023
  • “Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula”, Bronstein et al 2022
  • “Melting Pot 2.0”, Agapiou et al 2022
  • “Token Turing Machines”, Ryoo et al 2022
  • “Legged Locomotion in Challenging Terrains Using Egocentric Vision”, Agarwal et al 2022
  • “Over-Communicate No More: Situated RL Agents Learn Concise Communication Protocols”, Kalinowska et al 2022
  • “E3B: Exploration via Elliptical Episodic Bonuses”, Henaff et al 2022
  • “Hyperbolic Deep Reinforcement Learning”, Cetin et al 2022
  • “Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization”, Ramamurthy et al 2022
  • “Dynamic Prompt Learning via Policy Gradient for Semi-Structured Mathematical Reasoning”, Lu et al 2022
  • “Simplifying Model-Based RL: Learning Representations, Latent-Space Models, and Policies With One Objective (ALM)”, Ghugare et al 2022
  • “Human-Level Atari 200× Faster”, Kapturowski et al 2022
  • “Nearest Neighbor Non-Autoregressive Text Generation”, Niwa et al 2022
  • “A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning”, Dann et al 2022
  • “Learning to Generalize With Object-Centric Agents in the Open World Survival Game Crafter”, Stanić et al 2022
  • “Improved Policy Optimization for Online Imitation Learning”, Lavington et al 2022
  • “Offline RL for Natural Language Generation With Implicit Language Q Learning”, Snell et al 2022
  • “Fine-Grained Image Captioning With CLIP Reward”, Cho et al 2022
  • “Reward Bases: Instantaneous Reward Revaluation With Temporal Difference Learning”, Millidge et al 2022
  • “Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi”, Grooten et al 2022
  • “Quantifying and Alleviating Political Bias in Language Models”, Liu et al 2022c
  • “Machine Learning Helps Control Tokamak Plasmas”, Georgescu2022
  • “Retrieval-Augmented Reinforcement Learning”, Goyal et al 2022
  • “Policy Learning and Evaluation With Randomized Quasi-Monte Carlo”, Arnold et al 2022
  • “A Data-Driven Approach for Learning to Control Computers”, Humphreys et al 2022
  • “Magnetic Control of Tokamak Plasmas through Deep Reinforcement Learning”, Degrave et al 2022
  • “Why Should I Trust You, Bellman? The Bellman Error Is a Poor Replacement for Value Error”, Fujimoto et al 2022
  • “Learning Dynamics and Generalization in Deep Reinforcement Learning”, Lyle et al 2022
  • “Agile Locomotion via Model-Free Learning”, Margolis2022
  • “Amortized Noisy Channel Neural Machine Translation”, Pang et al 2021
  • “Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs”, Korkmaz2021
  • “Simple but Effective: CLIP Embeddings for Embodied AI”, Khandelwal et al 2021
  • “Offline Reinforcement Learning With Implicit Q-Learning (IQL)”, Kostrikov et al 2021
  • “Recurrent Model-Free RL Is a Strong Baseline for Many POMDPs”, Ni et al 2021
  • “DroQ: Dropout Q-Functions for Doubly Efficient Reinforcement Learning”, Hiraoka et al 2021
  • “Batch Size-Invariance for Policy Optimization”, Hilton et al 2021
  • “MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research”, Samvelyan et al 2021
  • “Bootstrapped Meta-Learning”, Flennerhag et al 2021
  • “Megaverse: Simulating Embodied Agents at One Million Experiences per Second”, Petrenko et al 2021
  • “PES: Unbiased Gradient Estimation in Unrolled Computation Graphs With Persistent Evolution Strategies”, Vicol et al 2021
  • “Multi-Task Curriculum Learning in a Complex, Visual, Hard-Exploration Domain: Minecraft”, Kanitscheider et al 2021
  • “On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning”, Vischer et al 2021
  • “Constructions in Combinatorics via Neural Networks”, Wagner2021
  • “Muesli: Combining Improvements in Policy Optimization”, Hessel et al 2021
  • “Podracer Architectures for Scalable Reinforcement Learning”, Hessel et al 2021
  • “Counter-Strike Deathmatch With Large-Scale Behavioral Cloning”, Pearce & Zhu2021
  • “ALD: Efficient Transformers in Reinforcement Learning Using Actor-Learner Distillation”, Parisotto & Salakhutdinov2021
  • “Replay in Deep Learning: Current Approaches and Missing Biological Elements”, Hayes et al 2021
  • “Large Batch Simulation for Deep Reinforcement Learning”, Shacklett et al 2021
  • “The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games”, Yu et al 2021
  • “Reinforcement Learning for Datacenter Congestion Control”, Tessler et al 2021
  • “Training Larger Networks for Deep Reinforcement Learning”, Ota et al 2021
  • “How RL Agents Behave When Their Actions Are Modified”, Langlois & Everitt2021
  • “A^✱ Search Without Expansions: Learning Heuristic Functions With Deep Q-Networks”, Agostinelli et al 2021
  • “Randomized Ensembled Double Q-Learning (REDQ): Learning Fast Without a Model”, Chen et al 2021
  • “MLGO: a Machine Learning Guided Compiler Optimizations Framework”, Trofin et al 2021
  • “Evolving Reinforcement Learning Algorithms”, Co-Reyes et al 2021
  • “Using Deep Reinforcement Learning to Reveal How the Brain Encodes Abstract State-Space Representations in High-Dimensional Environments”, Cross2020
  • “Autonomous Navigation of Stratospheric Balloons Using Reinforcement Learning”, Bellemare et al 2020
  • “A Unified Framework for Dopamine Signals across Timescales”, Kim et al 2020
  • “Offline Learning from Demonstrations and Unlabeled Experience”, Zolna et al 2020
  • “Adversarial Vulnerabilities of Human Decision-Making”, Dezfouli et al 2020
  • “D2RL: Deep Dense Architectures in Reinforcement Learning”, Sinha et al 2020
  • “Human-Centric Dialog Training via Offline Reinforcement Learning”, Jaques et al 2020
  • “Emergent Social Learning via Multi-Agent Reinforcement Learning”, Ndousse et al 2020
  • “Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning”, Fuchs et al 2020
  • “SPR: Data-Efficient Reinforcement Learning With Self-Predictive Representations”, Schwarzer et al 2020
  • “Learning Breakout From RAM—Part 2”, philoxenic 2020
  • “Learning Breakout From RAM—Part 1”, philoxenic 2020
  • “Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks”, Papoudakis et al 2020
  • “Improving GAN Training With Probability Ratio Clipping and Sample Reweighting”, Wu et al 2020
  • “Conservative Q-Learning for Offline Reinforcement Learning”, Kumar et al 2020
  • “Controlling Overestimation Bias With Truncated Mixture of Continuous Distributional Quantile Critics (TQC)”, Kuznetsov et al 2020
  • “Evaluating the Rainbow DQN Agent in Hanabi With Unseen Partners”, Canaan et al 2020
  • “Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels”, Kostrikov et al 2020
  • “Chip Placement With Deep Reinforcement Learning”, Mirhoseini et al 2020
  • “CURL: Contrastive Unsupervised Representations for Reinforcement Learning”, Srinivas et al 2020
  • “Evolving Normalization-Activation Layers”, Liu et al 2020
  • “Benchmarking End-To-End Behavioral Cloning on Video Games”, Kanervisto et al 2020
  • “Agent57: Outperforming the Atari Human Benchmark”, Badia et al 2020
  • “Deep Neuroethology of a Virtual Rodent”, Merel et al 2020
  • “Q^✱ Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison”, Xie & Jiang2020
  • “Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?”, Ota et al 2020
  • “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
  • “Combining Q-Learning and Search With Amortized Value Estimates”, Hamrick et al 2019
  • “SEED RL: Scalable and Efficient Deep-RL With Accelerated Central Inference”, Espeholt et al 2019
  • “Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?”, Du et al 2019
  • “QUARL: Quantized Reinforcement Learning (ActorQ)”, Lam et al 2019
  • “Deep Learning Enables Rapid Identification of Potent DDR1 Kinase Inhibitors”, Zhavoronkov et al 2019
  • “Exponential Slowdown for Larger Populations: The (μ+1)-EA on Monotone Functions”, Lengler & Zou2019
  • “Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the Playing Field”, Toromanoff et al 2019
  • “A View on Deep Reinforcement Learning in System Optimization”, Haj-Ali et al 2019
  • “Playing the Lottery With Rewards and Multiple Languages: Lottery Tickets in RL and NLP”, Yu et al 2019
  • “A General Dichotomy of Evolutionary Algorithms on Monotone Functions”, Lengler2019
  • “A Recipe for Training Neural Networks”, Karpathy2019
  • “Universal Quantum Control through Deep Reinforcement Learning”, Niu et al 2019
  • “Reinforcement Learning for Recommender Systems: A Case Study on Youtube”, Chen2019
  • “Benchmarking Classic and Learned Navigation in Complex 3D Environments”, Mishkin et al 2019
  • “AutoPhase: Compiler Phase-Ordering for High Level Synthesis With Deep Reinforcement Learning”, Haj-Ali et al 2019
  • “Anxiety, Depression, and Decision Making: A Computational Perspective”, Bishop & Gagne2019
  • “Reinforcement Learning in Artificial and Biological Systems”, Neftci & Averbeck2019
  • “Designing Neural Networks through Neuroevolution”, Stanley et al 2019
  • “IRLAS: Inverse Reinforcement Learning for Architecture Search”, Guo et al 2018
  • “Quantifying Generalization in Reinforcement Learning”, Cobbe et al 2018
  • “Top-K Off-Policy Correction for a REINFORCE Recommender System”, Chen et al 2018
  • “Relative Entropy Regularized Policy Iteration”, Abdolmaleki et al 2018
  • “ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware”, Cai et al 2018
  • “Neural Probabilistic Motor Primitives for Humanoid Control”, Merel et al 2018
  • “InstaNAS: Instance-Aware Neural Architecture Search”, Cheng et al 2018
  • “A Closer Look at Deep Policy Gradients”, Ilyas et al 2018
  • “One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets With RL”, Paine et al 2018
  • “Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow”, Peng et al 2018
  • “Learning to Perform Local Rewriting for Combinatorial Optimization”, Chen & Tian2018
  • “R2D2: Recurrent Experience Replay in Distributed Reinforcement Learning”, Kapturowski et al 2018
  • “Benchmarking Reinforcement Learning Algorithms on Real-World Robots”, Mahmood et al 2018
  • “Deterministic Implementations for Reproducibility in Deep Reinforcement Learning”, Nagarajan et al 2018
  • “Multi-Task Deep Reinforcement Learning With PopArt”, Hessel et al 2018
  • “Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction”, Hashimoto & Tsuruoka2018
  • “Searching Toward Pareto-Optimal Device-Aware Neural Architectures”, Cheng et al 2018
  • “A Study of Reinforcement Learning for Neural Machine Translation”, Wu et al 2018
  • “Improving Abstraction in Text Summarization”, Kryściński et al 2018
  • “Learning to Optimize Join Queries With Deep Reinforcement Learning”, Krishnan et al 2018
  • “InfoNCE: Representation Learning With Contrastive Predictive Coding (CPC)”, Oord et al 2018
  • “Is Q-Learning Provably Efficient?”, Jin et al 2018
  • “Maximum a Posteriori Policy Optimization”, Abdolmaleki et al 2018
  • “The Unusual Effectiveness of Averaging in GAN Training”, Yazıcı et al 2018
  • “Resource-Efficient Neural Architect”, Zhou et al 2018
  • “DVRL: Deep Variational Reinforcement Learning for POMDPs”, Igl et al 2018
  • “Playing Atari With Six Neurons”, Cuccu et al 2018
  • “Measuring the Intrinsic Dimension of Objective Landscapes”, Li et al 2018
  • “DP4G: Distributed Distributional Deterministic Policy Gradients”, Barth-Maron et al 2018
  • “Optimizing Query Evaluations Using Reinforcement Learning for Web Search”, Rosset et al 2018
  • “Delayed Impact of Fair Machine Learning”, Liu et al 2018
  • “Accelerated Methods for Deep Reinforcement Learning”, Stooke & Abbeel2018
  • “Learning Memory Access Patterns”, Hashemi et al 2018
  • “Investigating Human Priors for Playing Video Game”, Dubey et al 2018
  • “ME-TRPO: Model-Ensemble Trust-Region Policy Optimization”, Kurutach et al 2018
  • “TD3: Addressing Function Approximation Error in Actor-Critic Methods”, Fujimoto et al 2018
  • “Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration”, Liu et al 2018
  • “Unicorn: Continual Learning With a Universal, Off-Policy Agent”, Mankowitz et al 2018
  • “ENAS: Efficient Neural Architecture Search via Parameter Sharing”, Pham et al 2018
  • “Regularized Evolution for Image Classifier Architecture Search”, Real et al 2018
  • “IMPALA: Scalable Distributed Deep-RL With Importance Weighted Actor-Learner Architectures”, Espeholt et al 2018
  • “Interactive Grounded Language Acquisition and Generalization in a 2D World”, Yu et al 2018
  • “Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning With a Stochastic Actor”, Haarnoja et al 2018
  • “Chapter 5: Monte Carlo Methods”, Sutton & Barto2018 (page 133)
  • “Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning”, Such et al 2017
  • “The Case for Learned Index Structures”, Kraska et al 2017
  • “AI Safety Gridworlds”, Leike et al 2017
  • “Classification With Costly Features Using Deep Reinforcement Learning”, Janisch et al 2017
  • “Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection”, Kato & Shinozaki2017
  • “Towards the Use of Deep Reinforcement Learning With Global Policy For Query-Based Extractive Summarization”, Molla2017
  • “Swish: Searching for Activation Functions”, Ramachandran et al 2017
  • “Gradient-Free Policy Architecture Search and Adaptation”, Ebrahimi et al 2017
  • “Rainbow: Combining Improvements in Deep Reinforcement Learning”, Hessel et al 2017
  • “OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning”, Henderson et al 2017
  • “Deep Reinforcement Learning That Matters”, Henderson et al 2017
  • “Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents”, Machado et al 2017
  • “Seq2SQL: Generating Structured Queries from Natural Language Using Reinforcement Learning”, Zhong et al 2017
  • “The Successor Representation in Human Reinforcement Learning”, Momennejad et al 2017
  • “Practical Block-Wise Neural Network Architecture Generation”, Zhong et al 2017
  • “Learning Policies for Adaptive Tracking With Deep Feature Cascades”, Huang et al 2017
  • “Reinforced Video Captioning With Entailment Rewards”, Pasunuru & Bansal2017
  • “A Distributional Perspective on Reinforcement Learning”, Bellemare et al 2017
  • “Tracking As Online Decision-Making: Learning a Policy from Streaming Videos With Reinforcement Learning”, III & Ramanan2017
  • “Trial without Error: Towards Safe Reinforcement Learning via Human Intervention”, Saunders et al 2017
  • “Efficient Architecture Search by Network Transformation”, Cai et al 2017
  • “Grammatical Error Correction With Neural Reinforcement Learning”, Sakaguchi et al 2017
  • “Noisy Networks for Exploration”, Fortunato et al 2017
  • “Gated-Attention Architectures for Task-Oriented Language Grounding”, Chaplot et al 2017
  • “The Persistence and Transience of Memory”, Richards & Frankland2017
  • “Deep Reinforcement Learning from Human Preferences § Appendix A.2: Atari”, Christiano et al 2017 (page 15 org openai)
  • “Towards Synthesizing Complex Programs from Input-Output Examples”, Chen et al 2017
  • “IDK Cascades: Fast Deep Learning by Learning Not to Overthink”, Wang et al 2017
  • “Teaching Machines to Describe Images via Natural Language Feedback”, Ling & Fidler2017
  • “Learning Time/Memory-Efficient Deep Architectures With Budgeted Super Networks”, Veniat & Denoyer2017
  • “Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models”, Guimaraes et al 2017
  • “Ask the Right Questions: Active Question Reformulation With Reinforcement Learning”, Buck et al 2017
  • “A Deep Reinforced Model for Abstractive Summarization”, Paulus et al 2017
  • “Inferring and Executing Programs for Visual Reasoning”, Johnson et al 2017
  • “Time-Contrastive Networks: Self-Supervised Learning from Video”, Sermanet et al 2017
  • “RAM: Dynamic Computational Time for Visual Attention”, Li et al 2017
  • “Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (EPANNs)”, Soltoggio et al 2017
  • “Improving Neural Machine Translation With Conditional Sequence Generative Adversarial Nets”, Yang et al 2017
  • “End-To-End Optimization of Goal-Driven and Visually Grounded Dialogue Systems”, Strub et al 2017
  • “Neural Episodic Control”, Pritzel et al 2017
  • “CoDeepNEAT: Evolving Deep Neural Networks”, Miikkulainen et al 2017
  • “Tuning Recurrent Neural Networks With Reinforcement Learning”, Jaques et al 2017
  • “PathNet: Evolution Channels Gradient Descent in Super Neural Networks”, Fernando et al 2017
  • “The Kelly Coin-Flipping Game: Exact Solutions”, Gwern et al 2017
  • “Deep Reinforcement Learning: A Brief Survey”, Arulkumaran et al 2017
  • “Your TL;DR by an AI: A Deep Reinforced Model for Abstractive Summarization”, Paulus2017
  • “Loss Is Its Own Reward: Self-Supervision for Reinforcement Learning”, Shelhamer et al 2016
  • “Self-Critical Sequence Training for Image Captioning”, Rennie et al 2016
  • “Neural Combinatorial Optimization With Reinforcement Learning”, Bello et al 2016
  • “Reinforcement Learning With Unsupervised Auxiliary Tasks”, Jaderberg et al 2016
  • “A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models”, Finn et al 2016
  • “Hybrid Computing Using a Neural Network With Dynamic External Memory”, Graves et al 2016
  • “Connecting Generative Adversarial Networks and Actor-Critic Methods”, Pfau & Vinyals2016
  • “Deep Reinforcement Learning for Mention-Ranking Coreference Models”, Clark & Manning2016
  • “Deep Neural Networks for YouTube Recommendations”, Covington et al 2016
  • “The Malmo Platform for Artificial Intelligence Experimentation”, Johnson et al 2016
  • “Progressive Neural Networks”, Rusu et al 2016
  • “Learning to Optimize”, Li & Malik2016
  • “Deep Reinforcement Learning for Dialogue Generation”, Li et al 2016
  • “ViZDoom: A Doom-Based AI Research Platform for Visual Reinforcement Learning”, Kempka et al 2016
  • “Learning from the Memory of Atari 2600”, Sygnowski & Michalewski2016
  • “Improving Information Extraction by Acquiring External Evidence With Reinforcement Learning”, Narasimhan et al 2016
  • “Asynchronous Methods for Deep Reinforcement Learning”, Mnih et al 2016
  • “Dueling Network Architectures for Deep Reinforcement Learning”, Wang et al 2015
  • “Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning”, Parisotto et al 2015
  • “Prioritized Experience Replay”, Schaul et al 2015
  • “Deep Reinforcement Learning With Double Q-Learning”, Hasselt et al 2015
  • “Gorila: Massively Parallel Methods for Deep Reinforcement Learning”, Nair et al 2015
  • “Reinforcement Learning Neural Turing Machines—Revised”, Zaremba & Sutskever2015
  • “An Invitation to Imitation”, Bagnell2015
  • “TRPO: Trust Region Policy Optimization”, Schulman et al 2015
  • “DRAW: A Recurrent Neural Network For Image Generation”, Gregor et al 2015
  • “Random Feedback Weights Support Learning in Deep Neural Networks”, Lillicrap et al 2014
  • “Learning to Execute”, Zaremba & Sutskever2014
  • “Does Temporal Discounting Explain Unhealthy Behavior? A Systematic Review and Reinforcement Learning Perspective”, Story et al 2014
  • “Playing Atari With Deep Reinforcement Learning”, Mnih et al 2013
  • “The Arcade Learning Environment: An Evaluation Platform for General Agents”, Bellemare et al 2012
  • “Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting”, Xie et al 2012
  • “Off-Policy Actor-Critic”, Degris et al 2012
  • “Neural Mechanisms of Speed-Accuracy Tradeoff”, Heitz & Schall2012
  • “DAgger: A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning”, Ross et al 2010
  • “Compositional Pattern Producing Networks: A Novel Abstraction of Development”, Stanley2007
  • “Midbrain Dopamine Neurons Encode a Quantitative Reward Prediction Error Signal”, Bayer & Glimcher2005
  • “It Takes Two Neurons To Ride a Bicycle”, Cook2004
  • “Recent Developments in the Evolution of Morphologies and Controllers for Physically Simulated Creatures § A Re-Implementation of Sims’ Work Using the MathEngine Physics Engine”, Taylor & Massey2001 (page 6)
  • “Learning to Drive a Bicycle Using Reinforcement Learning and Shaping”, Randløv & Alstrøm1998
  • “6.6 Actor-Critic Methods”, Sutton & Barto1998
  • “Descriptor Predictive Control: Tracking Controllers for a Riderless Bicycle”, Wissel et al 1996
  • “Control for an Autonomous Bicycle”, Getz & Marsden1995
  • “Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning”, Williams1992
  • Proceedings of the First International Conference on Genetic Algorithms and Their Applications, Grefenstette1985
  • “Temporal Credit Assignment In Reinforcement Learning”, Sutton1984
  • “Experiments on the Mechanization of Game-Learning Part II. Rule-Based Learning and the Human Window [BOXES]”, Michie1982
  • “Why the Law of Effect Will Not Go Away”, Dennett1974
  • “Experiments on the Mechanization of Game-Learning Part I. Characterization of the Model and Its Parameters [MENACE]”, Michie1963
  • “A Matchbox Game-Learning Machine”, Gardner1962
  • “Some Studies in Machine Learning Using the Game of Checkers”, Samuel1959
  • “John Schulman’s Homepage”, Schulman2024
  • “LA Residents Complain about ‘Waze Craze’”
  • “Sutton & Barto Book: Reinforcement Learning: An Introduction”, Sutton & Barto2024
  • “Evolving Stable Strategies”, Ha2024
  • “Finding Nash Equilibria through Simulation”
  • Trackmania I—The History of Machine Learning in Trackmania
  • “The 37 Implementation Details of Proximal Policy Optimization”
  • “Microsoft and Meta Join Google in Using AI to Help Run Their Data Centers”
  • “Hedonic Loops and Taming RL”, Millidge2024
  • “Sony’s Racing Car AI Just Destroyed Its Human Competitors—By Being Nice (and Fast)”
  • “Target-Driven Visual Navigation in Indoor Scenes Using Deep Reinforcement Learning [Video]”
  • “Measuring the Intrinsic Dimension of Objective Landscapes [Video]”
  • “Zyme—An Evolvable Language”
  • Wikipedia
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