{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/reinforcement-learning-1/papers/149","list_of":"/task/reinforcement-learning-1","task":"Reinforcement Learning (RL)","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":149,"pages_in_order":152,"rows_per_page":100,"rows":[14801,14900],"of":15113,"counts":{"archive_papers_tagged":15113,"with_a_code_link":4749,"where_syntology_ran_a_sample":1416,"not_listed_spam_title":0,"listed":15113,"listed_where_code_ran":1416,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1186,"every_run_a_failure_of_syntologys_instrument":230,"listed_with_a_run_with_no_instrument_failure":1186,"listed_every_run_a_failure_of_syntologys_instrument":230,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/reinforcement-learning-1","prev":"/task/reinforcement-learning-1/papers/148","next":"/task/reinforcement-learning-1/papers/150","papers":[{"url":null,"slug":"regulating-reward-training-by-means-of","title":"Regulating Reward Training by Means of Certainty Prediction in a Neural Network-Implemented Pong Game","date":"2016-09-23","arxiv_id":"1609.07434","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-modular-neural-network-policies-for","title":"Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer","date":"2016-09-22","arxiv_id":"1609.07088","repositories_listed":0,"syntology":null},{"url":null,"slug":"modelling-stock-market-investors-as","title":"Modelling Stock-market Investors as Reinforcement Learning Agents [Correction]","date":"2016-09-20","arxiv_id":"1609.06086","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-deep-symbolic-reinforcement-learning","title":"Towards Deep Symbolic Reinforcement Learning","date":"2016-09-18","arxiv_id":"1609.05518","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploration-potential","title":"Exploration Potential","date":"2016-09-16","arxiv_id":"1609.04994","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-spoken-content-retrieval-by-deep","title":"Interactive Spoken Content Retrieval by Deep Reinforcement Learning","date":"2016-09-16","arxiv_id":"1609.05234","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-reinforcement-learning-a-survey","title":"Bayesian Reinforcement Learning: A Survey","date":"2016-09-14","arxiv_id":"1609.04436","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-evolution-in-populations-of-ideas","title":"Stochastic evolution in populations of ideas","date":"2016-09-14","arxiv_id":"1609.04443","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-centralized-reinforcement-learning-method","title":"A centralized reinforcement learning method for multi-agent job scheduling in Grid","date":"2016-09-11","arxiv_id":"1609.03157","repositories_listed":0,"syntology":null},{"url":null,"slug":"episodic-exploration-for-deep-deterministic","title":"Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks","date":"2016-09-10","arxiv_id":"1609.02993","repositories_listed":0,"syntology":null},{"url":null,"slug":"dialogue-manager-domain-adaptation-using","title":"Dialogue manager domain adaptation using Gaussian process reinforcement learning","date":"2016-09-09","arxiv_id":"1609.02846","repositories_listed":0,"syntology":null},{"url":null,"slug":"unifying-task-specification-in-reinforcement","title":"Unifying task specification in reinforcement learning","date":"2016-09-07","arxiv_id":"1609.01995","repositories_listed":0,"syntology":null},{"url":null,"slug":"reward-function-and-initial-values-better","title":"Reward Function and Initial Values: Better Choices for Accelerated Goal-Directed Reinforcement Learning","date":"2016-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"single-photon-in-hierarchical-architecture","title":"Single photon in hierarchical architecture for physical reinforcement learning: Photon intelligence","date":"2016-09-01","arxiv_id":"1609.00686","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-probabilistic-trajectory","title":"Adaptive Probabilistic Trajectory Optimization via Efficient Approximate Inference","date":"2016-08-22","arxiv_id":"1608.06235","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-human-reading-with-neural-attention","title":"Modeling Human Reading with Neural Attention","date":"2016-08-19","arxiv_id":"1608.05604","repositories_listed":0,"syntology":null},{"url":null,"slug":"bbq-networks-efficient-exploration-in-deep-1","title":"BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems","date":"2016-08-17","arxiv_id":"1608.05081","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-problem-approximate-planning-of-pomdps","title":"Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies","date":"2016-08-17","arxiv_id":"1608.04996","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-algorithms-for-regret","title":"Reinforcement Learning algorithms for regret minimization in structured Markov Decision Processes","date":"2016-08-17","arxiv_id":"1608.04929","repositories_listed":0,"syntology":null},{"url":null,"slug":"perceptual-reward-functions","title":"Perceptual Reward Functions","date":"2016-08-12","arxiv_id":"1608.03824","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuroevolution-based-inverse-reinforcement","title":"Neuroevolution-Based Inverse Reinforcement Learning","date":"2016-08-09","arxiv_id":"1608.02971","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-lower-bounds-for-regret-in-reinforcement","title":"On Lower Bounds for Regret in Reinforcement Learning","date":"2016-08-09","arxiv_id":"1608.02732","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-adaptation-of-deep-architectures-with","title":"Online Adaptation of Deep Architectures with Reinforcement Learning","date":"2016-08-08","arxiv_id":"1608.02292","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovering-latent-states-for-model-learning","title":"Discovering Latent States for Model Learning: Applying Sensorimotor Contingencies Theory and Predictive Processing to Model Context","date":"2016-08-01","arxiv_id":"1608.00359","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-organization-in-a-distributed","title":"Self-organization in a distributed coordination game through heuristic rules","date":"2016-07-31","arxiv_id":"1608.00213","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-sensorimotor-reinforcement-learning","title":"A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction","date":"2016-07-27","arxiv_id":"1607.07939","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-stochastic-composition","title":"Accelerating Stochastic Composition Optimization","date":"2016-07-25","arxiv_id":"1607.07329","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-cost-sensitive-feature-acquisition","title":"Sequential Cost-Sensitive Feature Acquisition","date":"2016-07-13","arxiv_id":"1607.03691","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-bridge-bidding-using-deep","title":"Automatic Bridge Bidding Using Deep Reinforcement Learning","date":"2016-07-12","arxiv_id":"1607.03290","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-is-posterior-sampling-better-than","title":"Why is Posterior Sampling Better than Optimism for Reinforcement Learning?","date":"2016-07-01","arxiv_id":"1607.00215","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-the-bellman-residual-a-bad-proxy","title":"Is the Bellman residual a bad proxy?","date":"2016-06-24","arxiv_id":"1606.07636","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-preprocessing-for-tactile-data","title":"Unsupervised preprocessing for Tactile Data","date":"2016-06-23","arxiv_id":"1606.07312","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-control-and-human-feedback-in","title":"Simultaneous Control and Human Feedback in the Training of a Robotic Agent with Actor-Critic Reinforcement Learning","date":"2016-06-22","arxiv_id":"1606.06979","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hierarchical-reinforcement-learning-method","title":"A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks","date":"2016-06-20","arxiv_id":"1606.06355","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-reward-function-for-survival","title":"On Reward Function for Survival","date":"2016-06-18","arxiv_id":"1606.05767","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-reinforcement-learning-discovers","title":"Deep Reinforcement Learning Discovers Internal Models","date":"2016-06-16","arxiv_id":"1606.05174","repositories_listed":0,"syntology":null},{"url":null,"slug":"successor-features-for-transfer-in","title":"Successor Features for Transfer in Reinforcement Learning","date":"2016-06-16","arxiv_id":"1606.05312","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-reinforcement-learning-with-macro","title":"Deep Reinforcement Learning With Macro-Actions","date":"2016-06-15","arxiv_id":"1606.04615","repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-generation-as-planning-under","title":"Natural Language Generation as Planning under Uncertainty Using Reinforcement Learning","date":"2016-06-15","arxiv_id":"1606.04686","repositories_listed":0,"syntology":null},{"url":null,"slug":"policy-networks-with-two-stage-training-for","title":"Policy Networks with Two-Stage Training for Dialogue Systems","date":"2016-06-10","arxiv_id":"1606.03152","repositories_listed":0,"syntology":null},{"url":null,"slug":"face-valuing-training-user-interfaces-with","title":"Face valuing: Training user interfaces with facial expressions and reinforcement learning","date":"2016-06-09","arxiv_id":"1606.02807","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuously-learning-neural-dialogue","title":"Continuously Learning Neural Dialogue Management","date":"2016-06-08","arxiv_id":"1606.02689","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-sampling-interval-of-sensor-networks","title":"Adapting Sampling Interval of Sensor Networks Using On-Line Reinforcement Learning","date":"2016-06-07","arxiv_id":"1606.02193","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-optimize","title":"Learning to Optimize","date":"2016-06-06","arxiv_id":"1606.01885","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-q-networks-for-accelerating-the-training","title":"Deep Q-Networks for Accelerating the Training of Deep Neural Networks","date":"2016-06-05","arxiv_id":"1606.01467","repositories_listed":0,"syntology":null},{"url":null,"slug":"difference-of-convex-functions-programming","title":"Difference of Convex Functions Programming Applied to Control with Expert Data","date":"2016-06-03","arxiv_id":"1606.01128","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-lstm-based-dialog-control","title":"End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning","date":"2016-06-03","arxiv_id":"1606.01269","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-for-semantic","title":"Reinforcement Learning for Semantic Segmentation in Indoor Scenes","date":"2016-06-03","arxiv_id":"1606.01178","repositories_listed":0,"syntology":null},{"url":null,"slug":"death-and-suicide-in-universal-artificial","title":"Death and Suicide in Universal Artificial Intelligence","date":"2016-06-02","arxiv_id":"1606.00652","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-for-visual-object","title":"Reinforcement Learning for Visual Object Detection","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"information-theoretically-aided-reinforcement","title":"Information Theoretically Aided Reinforcement Learning for Embodied Agents","date":"2016-05-31","arxiv_id":"1605.09735","repositories_listed":0,"syntology":null},{"url":null,"slug":"control-of-memory-active-perception-and","title":"Control of Memory, Active Perception, and Action in Minecraft","date":"2016-05-30","arxiv_id":"1605.09128","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-free-imitation-learning-with-policy","title":"Model-Free Imitation Learning with Policy Optimization","date":"2016-05-26","arxiv_id":"1605.08478","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-pac-rl-algorithm-for-episodic-pomdps","title":"A PAC RL Algorithm for Episodic POMDPs","date":"2016-05-25","arxiv_id":"1605.08062","repositories_listed":0,"syntology":null},{"url":null,"slug":"localizing-by-describing-attribute-guided","title":"Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition","date":"2016-05-20","arxiv_id":"1605.06217","repositories_listed":0,"syntology":null},{"url":null,"slug":"option-discovery-in-hierarchical","title":"Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering","date":"2016-05-17","arxiv_id":"1605.05359","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-reinforcement-learning-system-to-encourage","title":"A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients","date":"2016-05-13","arxiv_id":"1605.04070","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-human-interpretable-dialog","title":"Optimizing human-interpretable dialog management policy using Genetic Algorithm","date":"2016-05-12","arxiv_id":"1605.03915","repositories_listed":0,"syntology":null},{"url":null,"slug":"avoiding-wireheading-with-value-reinforcement","title":"Avoiding Wireheading with Value Reinforcement Learning","date":"2016-05-10","arxiv_id":"1605.03143","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-options-for-deep-reinforcement","title":"Classifying Options for Deep Reinforcement Learning","date":"2016-04-27","arxiv_id":"1604.08153","repositories_listed":0,"syntology":null},{"url":null,"slug":"tournament-selection-in-zeroth-level","title":"Tournament selection in zeroth-level classifier systems based on average reward reinforcement learning","date":"2016-04-26","arxiv_id":"1604.07704","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-reinforcement-learning-to-validate","title":"Using Reinforcement Learning to Validate Empirical Game-Theoretic Analysis: A Continuous Double Auction Study","date":"2016-04-22","arxiv_id":"1604.06710","repositories_listed":0,"syntology":null},{"url":null,"slug":"inverse-reinforcement-learning-with-1","title":"Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics","date":"2016-04-13","arxiv_id":"1604.03912","repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretically-grounded-policy-advice-from","title":"Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer","date":"2016-04-13","arxiv_id":"1604.03986","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-statistical-learning-strategy-for-closed","title":"A statistical learning strategy for closed-loop control of fluid flows","date":"2016-04-11","arxiv_id":"1604.03392","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-based-local-search-for","title":"Reinforcement learning based local search for grouping problems: A case study on graph coloring","date":"2016-04-01","arxiv_id":"1604.00377","repositories_listed":0,"syntology":null},{"url":null,"slug":"algorithms-for-batch-hierarchical","title":"Algorithms for Batch Hierarchical Reinforcement Learning","date":"2016-03-29","arxiv_id":"1603.08869","repositories_listed":0,"syntology":null},{"url":null,"slug":"negative-learning-rates-and-p-learning","title":"Negative Learning Rates and P-Learning","date":"2016-03-27","arxiv_id":"1603.08253","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-parameter-selection-in-evolutionary","title":"Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range","date":"2016-03-22","arxiv_id":"1603.06788","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-convolutional-attention-networks-for","title":"Fully Convolutional Attention Networks for Fine-Grained Recognition","date":"2016-03-22","arxiv_id":"1603.06765","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-as-a-multiagent","title":"Feature Selection as a Multiagent Coordination Problem","date":"2016-03-16","arxiv_id":"1603.05152","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-signaling-game-approach-to-databases","title":"A Signaling Game Approach to Databases Querying and Interaction","date":"2016-03-13","arxiv_id":"1603.04068","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-linearly-solvable-markov","title":"Hierarchical Linearly-Solvable Markov Decision Problems","date":"2016-03-10","arxiv_id":"1603.03267","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentially-private-policy-evaluation","title":"Differentially Private Policy Evaluation","date":"2016-03-07","arxiv_id":"1603.02010","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-shared-representations-in-multi-task","title":"Learning Shared Representations in Multi-task Reinforcement Learning","date":"2016-03-07","arxiv_id":"1603.02041","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-decision-making-in-electricity","title":"Hierarchical Decision Making In Electricity Grid Management","date":"2016-03-06","arxiv_id":"1603.01840","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-within-projective-simulation","title":"Meta-learning within Projective Simulation","date":"2016-02-25","arxiv_id":"1602.08017","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-of-pomdps-using","title":"Reinforcement Learning of POMDPs using Spectral Methods","date":"2016-02-25","arxiv_id":"1602.07764","repositories_listed":0,"syntology":null},{"url":null,"slug":"thompson-sampling-is-asymptotically-optimal","title":"Thompson Sampling is Asymptotically Optimal in General Environments","date":"2016-02-25","arxiv_id":"1602.07905","repositories_listed":0,"syntology":null},{"url":"/paper/learning-values-across-many-orders-of","slug":"learning-values-across-many-orders-of","title":"Learning values across many orders of magnitude","date":"2016-02-24","arxiv_id":"1602.07714","repositories_listed":0,"syntology":null},{"url":null,"slug":"policy-error-bounds-for-model-based","title":"Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models","date":"2016-02-19","arxiv_id":"1602.06346","repositories_listed":0,"syntology":null},{"url":null,"slug":"inverse-reinforcement-learning-in-swarm","title":"Inverse Reinforcement Learning in Swarm Systems","date":"2016-02-17","arxiv_id":"1602.05450","repositories_listed":0,"syntology":null},{"url":null,"slug":"pomdp-lite-for-robust-robot-planning-under","title":"POMDP-lite for Robust Robot Planning under Uncertainty","date":"2016-02-16","arxiv_id":"1602.04875","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-approach-for-real-time","title":"Reinforcement Learning approach for Real Time Strategy Games Battle city and S3","date":"2016-02-16","arxiv_id":"1602.04936","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-efficient-reinforcement-learning-in-1","title":"Data-Efficient Reinforcement Learning in Continuous-State POMDPs","date":"2016-02-08","arxiv_id":"1602.02523","repositories_listed":0,"syntology":null},{"url":null,"slug":"graying-the-black-box-understanding-dqns","title":"Graying the black box: Understanding DQNs","date":"2016-02-08","arxiv_id":"1602.02658","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-communicate-to-solve-riddles-with","title":"Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks","date":"2016-02-08","arxiv_id":"1602.02672","repositories_listed":0,"syntology":null},{"url":null,"slug":"pac-reinforcement-learning-with-rich","title":"PAC Reinforcement Learning with Rich Observations","date":"2016-02-08","arxiv_id":"1602.02722","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-information-acquisition","title":"Active Information Acquisition","date":"2016-02-05","arxiv_id":"1602.02181","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-machine-learning-with-glow-for","title":"Quantum machine learning with glow for episodic tasks and decision games","date":"2016-01-27","arxiv_id":"1601.07358","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-resolving-unidentifiability-in","title":"Towards Resolving Unidentifiability in Inverse Reinforcement Learning","date":"2016-01-25","arxiv_id":"1601.06569","repositories_listed":0,"syntology":null},{"url":null,"slug":"inverse-reinforcement-learning-via-deep","title":"Inverse Reinforcement Learning via Deep Gaussian Process","date":"2015-12-26","arxiv_id":"1512.08065","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-comparison-of-neural","title":"An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments","date":"2015-12-17","arxiv_id":"1512.05509","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-discount-deep-reinforcement-learning","title":"How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies","date":"2015-12-07","arxiv_id":"1512.02011","repositories_listed":0,"syntology":null},{"url":null,"slug":"risk-constrained-reinforcement-learning-with","title":"Risk-Constrained Reinforcement Learning with Percentile Risk Criteria","date":"2015-12-05","arxiv_id":"1512.01629","repositories_listed":0,"syntology":null},{"url":null,"slug":"q-networks-for-binary-vector-actions","title":"Q-Networks for Binary Vector Actions","date":"2015-12-04","arxiv_id":"1512.01332","repositories_listed":0,"syntology":null},{"url":null,"slug":"inverse-reinforcement-learning-with-locally","title":"Inverse Reinforcement Learning with Locally Consistent Reward Functions","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-class-multi-annotator-active-learning","title":"Multi-Class Multi-Annotator Active Learning With Robust Gaussian Process for Visual Recognition","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-learning-to-think-algorithmic-information","title":"On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models","date":"2015-11-30","arxiv_id":"1511.09249","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-applied-to-an-electric","title":"Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice","date":"2015-11-29","arxiv_id":"1512.00408","repositories_listed":0,"syntology":null}],"record_sha256":"3ac9307a46ad6135b302c97e0231edce5174366810e3664bf131778765f20dc4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}