## Stem reinforc$ (all stems)

### 211 papers:

- CASE-2015-AntonelloGM #detection #fault
- Autonomous robotic system for thermographic detection of defects in upper layers of carbon fiber reinforced polymers (MA, SG, EM), pp. 634–639.
- CASE-2015-LiX #energy #learning #multi
- A multi-grid reinforcement learning method for energy conservation and comfort of HVAC in buildings (BL, LX), pp. 444–449.
- DATE-2015-ChenM #distributed #learning #manycore #optimisation #performance
- Distributed reinforcement learning for power limited many-core system performance optimization (ZC, DM), pp. 1521–1526.
- DHM-HM-2015-KurataniHHKUGH #analysis #comparison #process
- Expert vs. Elementary Skill Comparison and Process Analysis in VaRTM-Manufactured Carbon Fiber Reinforced Composites (YK, KH, TH, TK, TU, AG, HH), pp. 133–142.
- ICML-2015-Bou-AmmarTE #learning #policy #sublinear
- Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret (HBA, RT, EE), pp. 2361–2369.
- ICML-2015-JiangKS #abstraction #learning #modelling
- Abstraction Selection in Model-based Reinforcement Learning (NJ, AK, SS), pp. 179–188.
- ICML-2015-LakshmananOR #bound #learning
- Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning (KL, RO, DR), pp. 524–532.
- CASE-2014-HwangLW #adaptation #learning
- Adaptive reinforcement learning in box-pushing robots (KSH, JLL, WHW), pp. 1182–1187.
- DAC-2014-0001SMAKV #manycore #optimisation
- Reinforcement Learning-Based Inter- and Intra-Application Thermal Optimization for Lifetime Improvement of Multicore Systems (AD, RAS, GVM, BMAH, AK, BV), p. 6.
- DHM-2014-KikuchiTTGH #information management
- Biomechanics Investigation of Skillful Technician in Spray-up Fabrication Method — Converting Tacit Knowledge to Explicit Knowledge in the Fiber Reinforced Plastics Molding (TK, YT, YT, AG, HH), pp. 24–34.
- ICML-c2-2014-BrunskillL #learning
- PAC-inspired Option Discovery in Lifelong Reinforcement Learning (EB, LL), pp. 316–324.
- ICML-c2-2014-GrandeWH #learning #performance #process
- Sample Efficient Reinforcement Learning with Gaussian Processes (RCG, TJW, JPH), pp. 1332–1340.
- ICML-c2-2014-QinLJ #learning #optimisation
- Sparse Reinforcement Learning via Convex Optimization (ZQ, WL, FJ), pp. 424–432.
- ICML-c1-2013-0005LSL #feature model #learning #modelling #online
- Online Feature Selection for Model-based Reinforcement Learning (TTN, ZL, TS, TYL), pp. 498–506.
- ICML-c1-2013-MaillardNOR #bound #learning #representation
- Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning (OAM, PN, RO, DR), pp. 543–551.
- ICML-c3-2013-DimitrakakisT #learning
- ABC Reinforcement Learning (CD, NT), pp. 684–692.
- ICML-c3-2013-LattimoreHS #learning
- The Sample-Complexity of General Reinforcement Learning (TL, MH, PS), pp. 28–36.
- ICML-c3-2013-SilverNBWM #concurrent #interactive #learning
- Concurrent Reinforcement Learning from Customer Interactions (DS, LN, DB, SW, JM), pp. 924–932.
- SIGIR-2013-0001MMNGC #retrieval
- Self reinforcement for important passage retrieval (RR, LM, DMdM, JPN, AG, JGC), pp. 845–848.
- RE-2013-SultanovH #learning #requirements
- Application of reinforcement learning to requirements engineering: requirements tracing (HS, JHH), pp. 52–61.
- SAC-2013-LinCLG #approach #data-driven #distributed #learning #predict
- Distributed dynamic data driven prediction based on reinforcement learning approach (SYL, KMC, CCL, NG), pp. 779–784.
- ICEIS-v1-2012-RibeiroFBBDKE #algorithm #approach #learning
- Unified Algorithm to Improve Reinforcement Learning in Dynamic Environments — An Instance-based Approach (RR, FF, MACB, APB, OBD, ALK, FE), pp. 229–238.
- CIKM-2012-ChaliHI #learning #performance
- Improving the performance of the reinforcement learning model for answering complex questions (YC, SAH, KI), pp. 2499–2502.
- CIKM-2012-JiangSZ #effectiveness #ranking #towards
- Towards an effective and unbiased ranking of scientific literature through mutual reinforcement (XJ, XS, HZ), pp. 714–723.
- CIKM-2012-YanWLZCL #image #summary #timeline #visualisation
- Visualizing timelines: evolutionary summarization via iterative reinforcement between text and image streams (RY, XW, ML, WXZ, PJC, XL), pp. 275–284.
- ICML-2012-AzarMK #complexity #generative #learning #on the
- On the Sample Complexity of Reinforcement Learning with a Generative Model (MGA, RM, BK), p. 222.
- ICML-2012-Painter-WakefieldP #algorithm #learning
- Greedy Algorithms for Sparse Reinforcement Learning (CPW, RP), p. 114.
- ICML-2012-PiresS #estimation #learning #linear #statistics
- Statistical linear estimation with penalized estimators: an application to reinforcement learning (BAP, CS), p. 228.
- ICML-2012-RossB #identification #learning #modelling
- Agnostic System Identification for Model-Based Reinforcement Learning (SR, DB), p. 247.
- ICML-2012-WangWHL #learning #monte carlo
- Monte Carlo Bayesian Reinforcement Learning (YW, KSW, DH, WSL), p. 105.
- ICML-2012-XieHS #approach #automation #generative #learning
- Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting (NX, HH, MS), p. 139.
- KMIS-2012-HamadaAS #generative #learning #using
- A Generation Method of Reference Operation using Reinforcement Learning on Project Manager Skill-up Simulator (KH, MA, MS), pp. 15–20.
- DAC-2011-WangXAP #classification #learning #policy #power management #using
- Deriving a near-optimal power management policy using model-free reinforcement learning and Bayesian classification (YW, QX, ACA, MP), pp. 41–46.
- CASE-2010-DoroodgarN #architecture #learning
- A hierarchical reinforcement learning based control architecture for semi-autonomous rescue robots in cluttered environments (BD, GN), pp. 948–953.
- DATE-2010-YeHL #fault #multi
- Diagnosis of multiple arbitrary faults with mask and reinforcement effect (JY, YH, XL), pp. 885–890.
- CHI-2010-Villamarin-SalomonB #behaviour #using
- Using reinforcement to strengthen users’ secure behaviors (RVS, JCB), pp. 363–372.
- ICML-2010-LazaricG #learning #multi
- Bayesian Multi-Task Reinforcement Learning (AL, MG), pp. 599–606.
- ICML-2010-LizotteBM #analysis #learning #multi #performance #random
- Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis (DJL, MHB, SAM), pp. 695–702.
- ICML-2010-Mahmud #learning
- Constructing States for Reinforcement Learning (MMHM), pp. 727–734.
- ICML-2010-MorimuraSKHT #approximate #learning #parametricity
- Nonparametric Return Distribution Approximation for Reinforcement Learning (TM, MS, HK, HH, TT), pp. 799–806.
- ICML-2010-SzitaS #bound #complexity #learning #modelling
- Model-based reinforcement learning with nearly tight exploration complexity bounds (IS, CS), pp. 1031–1038.
- ICPR-2010-CohenP #learning #performance #robust
- Reinforcement Learning for Robust and Efficient Real-World Tracking (AC, VP), pp. 2989–2992.
- KDD-2010-AbeMPRJTBACKDG #learning #optimisation #using
- Optimizing debt collections using constrained reinforcement learning (NA, PM, CP, CKR, DLJ, VPT, JJB, GFA, BRC, MK, MD, TG), pp. 75–84.
- SEKE-2010-JuniorLAMW #impact analysis #learning #multi #using
- Impact Analysis Model for Brasília Area Control Center using Multi-agent System with Reinforcement Learning (ACdAJ, AFL, CRFdA, ACMAdM, LW), pp. 499–502.
- ICML-2009-DiukLL #adaptation #feature model #learning #problem
- The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning (CD, LL, BRL), pp. 249–256.
- ICML-2009-Niv #learning #summary #tutorial
- Tutorial summary: The neuroscience of reinforcement learning (YN), p. 16.
- ICML-2009-TaylorP #approximate #kernel #learning
- Kernelized value function approximation for reinforcement learning (GT, RP), pp. 1017–1024.
- ICML-2009-VlassisT #learning
- Model-free reinforcement learning as mixture learning (NV, MT), pp. 1081–1088.
- KMIS-2009-ZyglarskiB #documentation #keyword #network
- Scientific Documents Management System — Application of Kohonens Neural Networks with Reinforcement in Keywords Extraction (BZ, PB), pp. 55–62.
- HPDC-2009-Reeuwijk #data flow #framework #learning #named #peer-to-peer #self #using
- Maestro: a self-organizing peer-to-peer dataflow framework using reinforcement learning (CvR), pp. 187–196.
- CASE-2008-StabelliniZ #approach #learning #network #self
- Interference aware self-organization for wireless sensor networks: A reinforcement learning approach (LS, JZ), pp. 560–565.
- ICML-2008-DiukCL #learning #object-oriented #performance #representation
- An object-oriented representation for efficient reinforcement learning (CD, AC, MLL), pp. 240–247.
- ICML-2008-DoshiPR #learning #using
- Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs (FD, JP, NR), pp. 256–263.
- ICML-2008-EpshteynVD #learning
- Active reinforcement learning (AE, AV, GD), pp. 296–303.
- ICML-2008-FrankMP #learning
- Reinforcement learning in the presence of rare events (JF, SM, DP), pp. 336–343.
- ICML-2008-LazaricRB #learning
- Transfer of samples in batch reinforcement learning (AL, MR, AB), pp. 544–551.
- ICML-2008-MeloMR #analysis #approximate #learning
- An analysis of reinforcement learning with function approximation (FSM, SPM, MIR), pp. 664–671.
- ICML-2008-ParrLTPL #analysis #approximate #feature model #learning #linear #modelling
- An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning (RP, LL, GT, CPW, MLL), pp. 752–759.
- ICML-2008-ReisingerSM #kernel #learning #online
- Online kernel selection for Bayesian reinforcement learning (JR, PS, RM), pp. 816–823.
- ICML-2008-SakumaKW #learning #privacy
- Privacy-preserving reinforcement learning (JS, SK, RNW), pp. 864–871.
- SIGIR-2008-WeiLLH #multi #query #summary
- Query-sensitive mutual reinforcement chain and its application in query-oriented multi-document summarization (FW, WL, QL, YH), pp. 283–290.
- OOPSLA-2008-SimpkinsBIM #adaptation #learning #programming language #towards
- Towards adaptive programming: integrating reinforcement learning into a programming language (CS, SB, CLIJ, MM), pp. 603–614.
- RE-2008-SmithG #requirements
- Gameplay to Introduce and Reinforce Requirements Engineering Practices (RS, OG), pp. 95–104.
- SAC-2008-TierneyJ #ontology #semantics #using
- C-SAW---contextual semantic alignment of ontologies: using negative semantic reinforcement (BT, MJ), pp. 2346–2347.
- ITiCSE-2007-FreireFPT #education #using #web
- Using screen readers to reinforce web accessibility education (APF, RPdMF, DMBP, MAST), pp. 82–86.
- ICML-2007-PetersS #learning
- Reinforcement learning by reward-weighted regression for operational space control (JP, SS), pp. 745–750.
- ICML-2007-PhuaF #approximate #learning #linear
- Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation (CWP, RF), pp. 751–758.
- ICML-2007-TaylorS #learning
- Cross-domain transfer for reinforcement learning (MET, PS), pp. 879–886.
- ICML-2007-WilsonFRT #approach #learning #multi
- Multi-task reinforcement learning: a hierarchical Bayesian approach (AW, AF, SR, PT), pp. 1015–1022.
- ICML-2007-ZhangAV #learning #multi #random
- Conditional random fields for multi-agent reinforcement learning (XZ, DA, SVNV), pp. 1143–1150.
- RecSys-2007-TaghipourKG #approach #learning #recommendation #web
- Usage-based web recommendations: a reinforcement learning approach (NT, AAK, SSG), pp. 113–120.
- ICML-2006-AbbeelQN #learning #modelling #using
- Using inaccurate models in reinforcement learning (PA, MQ, AYN), pp. 1–8.
- ICML-2006-DegrisSW #learning #markov #problem #process
- Learning the structure of Factored Markov Decision Processes in reinforcement learning problems (TD, OS, PHW), pp. 257–264.
- ICML-2006-EpshteynD #learning
- Qualitative reinforcement learning (AE, GD), pp. 305–312.
- ICML-2006-KellerMP #approximate #automation #learning #programming
- Automatic basis function construction for approximate dynamic programming and reinforcement learning (PWK, SM, DP), pp. 449–456.
- ICML-2006-KonidarisB #information management #learning
- Autonomous shaping: knowledge transfer in reinforcement learning (GK, AGB), pp. 489–496.
- ICML-2006-NevmyvakaFK #execution #learning
- Reinforcement learning for optimized trade execution (YN, YF, MK), pp. 673–680.
- ICML-2006-PoupartVHR #learning
- An analytic solution to discrete Bayesian reinforcement learning (PP, NAV, JH, KR), pp. 697–704.
- ICML-2006-StrehlLWLL #learning
- PAC model-free reinforcement learning (ALS, LL, EW, JL, MLL), pp. 881–888.
- ICPR-v4-2006-ZhengLL #learning #network
- Control Double Inverted Pendulum by Reinforcement Learning with Double CMAC Network (YZ, SL, ZL), pp. 639–642.
- FATES-RV-2006-VeanesRC #learning #online #testing
- Online Testing with Reinforcement Learning (MV, PR, CC), pp. 240–253.
- ITiCSE-2005-Cox #approach #functional #human-computer #programming
- A pragmatic HCI approach: engagement by reinforcing perception with functional dsesign and programming (DC), pp. 39–43.
- ICEIS-v2-2005-LokugeA #hybrid #learning #multi
- Handling Multiple Events in Hybrid BDI Agents with Reinforcement Learning: A Container Application (PL, DA), pp. 83–90.
- ICML-2005-AbbeelN #learning
- Exploration and apprenticeship learning in reinforcement learning (PA, AYN), pp. 1–8.
- ICML-2005-EngelMM #learning #process
- Reinforcement learning with Gaussian processes (YE, SM, RM), pp. 201–208.
- ICML-2005-GroisW #approach #comprehension #learning
- Learning strategies for story comprehension: a reinforcement learning approach (EG, DCW), pp. 257–264.
- ICML-2005-LangfordZ #classification #learning #performance
- Relating reinforcement learning performance to classification performance (JL, BZ), pp. 473–480.
- ICML-2005-Mahadevan #learning
- Proto-value functions: developmental reinforcement learning (SM), pp. 553–560.
- ICML-2005-MichelsSN #learning #using
- High speed obstacle avoidance using monocular vision and reinforcement learning (JM, AS, AYN), pp. 593–600.
- ICML-2005-NatarajanT #learning #multi
- Dynamic preferences in multi-criteria reinforcement learning (SN, PT), pp. 601–608.
- ICML-2005-SimsekWB #clustering #graph #identification #learning
- Identifying useful subgoals in reinforcement learning by local graph partitioning (ÖS, APW, AGB), pp. 816–823.
- MLDM-2005-KuhnertK #feedback #learning
- Autonomous Vehicle Steering Based on Evaluative Feedback by Reinforcement Learning (KDK, MK), pp. 405–414.
- MLDM-2005-SilvaJNP #geometry #learning #metric #using
- Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures (ACS, VRdSJ, AdAN, ACdP), pp. 295–304.
- SAC-2005-KatayamaKN #learning #process
- Reinforcement learning agents with primary knowledge designed by analytic hierarchy process (KK, TK, HN), pp. 14–21.
- SAC-2005-TebriBC #incremental #learning
- Incremental profile learning based on a reinforcement method (HT, MB, CC), pp. 1096–1101.
- ICML-2004-MannorMHK #abstraction #clustering #learning
- Dynamic abstraction in reinforcement learning via clustering (SM, IM, AH, UK).
- ICML-2004-MerkeS #approximate #convergence #learning #linear
- Convergence of synchronous reinforcement learning with linear function approximation (AM, RS).
- ICML-2004-MoralesS #behaviour #learning
- Learning to fly by combining reinforcement learning with behavioural cloning (EFM, CS).
- ICML-2004-PieterN #learning
- Apprenticeship learning via inverse reinforcement learning (PA, AYN).
- ICML-2004-RudarySP #adaptation #constraints #learning #reasoning
- Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning (MRR, SPS, MEP).
- ICML-2004-SimsekB #abstraction #identification #learning #using
- Using relative novelty to identify useful temporal abstractions in reinforcement learning (ÖS, AGB).
- ICPR-v2-2004-LiuS #learning
- Reinforcement Learning-Based Feature Learning for Object Tracking (FL, JS), pp. 748–751.
- KDD-2004-AbeVAS #learning
- Cross channel optimized marketing by reinforcement learning (NA, NKV, CA, RS), pp. 767–772.
- ICML-2003-DriessensR #learning #relational
- Relational Instance Based Regression for Relational Reinforcement Learning (KD, JR), pp. 123–130.
- ICML-2003-Even-DarMM #learning
- Action Elimination and Stopping Conditions for Reinforcement Learning (EED, SM, YM), pp. 162–169.
- ICML-2003-LagoudakisP #classification #learning
- Reinforcement Learning as Classification: Leveraging Modern Classifiers (MGL, RP), pp. 424–431.
- ICML-2003-LaudD #analysis #learning
- The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping (AL, GD), pp. 440–447.
- ICML-2003-RussellZ #learning
- Q-Decomposition for Reinforcement Learning Agents (SJR, AZ), pp. 656–663.
- ICML-2003-WangD #learning #modelling #policy
- Model-based Policy Gradient Reinforcement Learning (XW, TGD), pp. 776–783.
- ICML-2003-WiewioraCE #learning
- Principled Methods for Advising Reinforcement Learning Agents (EW, GWC, CE), pp. 792–799.
- SIGIR-2003-WangZCLTM #clustering #multi #named
- ReCoM: reinforcement clustering of multi-type interrelated data objects (JW, HJZ, ZC, HL, LT, WYM), pp. 274–281.
- ICML-2002-DietterichBMS #learning #probability #refinement
- Action Refinement in Reinforcement Learning by Probability Smoothing (TGD, DB, RLdM, CS), pp. 107–114.
- ICML-2002-DriessensD #learning #relational
- Integrating Experimentation and Guidance in Relational Reinforcement Learning (KD, SD), pp. 115–122.
- ICML-2002-GhavamzadehM #learning
- Hierarchically Optimal Average Reward Reinforcement Learning (MG, SM), pp. 195–202.
- ICML-2002-GuestrinLP #coordination #learning
- Coordinated Reinforcement Learning (CG, MGL, RP), pp. 227–234.
- ICML-2002-GuestrinPS #learning #modelling
- Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs (CG, RP, DS), pp. 235–242.
- ICML-2002-Hengst #learning
- Discovering Hierarchy in Reinforcement Learning with HEXQ (BH), pp. 243–250.
- ICML-2002-KakadeL #approximate #learning
- Approximately Optimal Approximate Reinforcement Learning (SK, JL), pp. 267–274.
- ICML-2002-LaudD #behaviour #learning
- Reinforcement Learning and Shaping: Encouraging Intended Behaviors (AL, GD), pp. 355–362.
- ICML-2002-MerkeS #approximate #convergence #learning
- A Necessary Condition of Convergence for Reinforcement Learning with Function Approximation (AM, RS), pp. 411–418.
- ICML-2002-OLZ #learning #using
- Stock Trading System Using Reinforcement Learning with Cooperative Agents (JO, JWL, BTZ), pp. 451–458.
- ICML-2002-PickettB #algorithm #learning #named
- PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning (MP, AGB), pp. 506–513.
- ICML-2002-Ryan #automation #behaviour #learning #modelling #using
- Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning Hierarchies (MRKR), pp. 522–529.
- ICML-2002-SeriT #learning #modelling
- Model-based Hierarchical Average-reward Reinforcement Learning (SS, PT), pp. 562–569.
- KDD-2002-PednaultAZ #learning
- Sequential cost-sensitive decision making with reinforcement learning (EPDP, NA, BZ), pp. 259–268.
- SIGIR-2002-Zha #clustering #summary #using
- Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering (HZ), pp. 113–120.
- ICML-2001-Geibel #bound #learning
- Reinforcement Learning with Bounded Risk (PG), pp. 162–169.
- ICML-2001-GhavamzadehM #learning
- Continuous-Time Hierarchical Reinforcement Learning (MG, SM), pp. 186–193.
- ICML-2001-GlickmanS #learning #memory management #policy #probability #search-based
- Evolutionary Search, Stochastic Policies with Memory, and Reinforcement Learning with Hidden State (MRG, KPS), pp. 194–201.
- ICML-2001-McGovernB #automation #learning #using
- Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density (AM, AGB), pp. 361–368.
- ICML-2001-PerkinsB #learning #set
- Lyapunov-Constrained Action Sets for Reinforcement Learning (TJP, AGB), pp. 409–416.
- ICML-2001-SatoK #learning #markov #problem
- Average-Reward Reinforcement Learning for Variance Penalized Markov Decision Problems (MS, SK), pp. 473–480.
- ICML-2001-StoneS #learning #scalability #towards
- Scaling Reinforcement Learning toward RoboCup Soccer (PS, RSS), pp. 537–544.
- ICML-2001-Wiering #learning #using
- Reinforcement Learning in Dynamic Environments using Instantiated Information (MW), pp. 585–592.
- ICML-2001-Wyatt #learning #using
- Exploration Control in Reinforcement Learning using Optimistic Model Selection (JLW), pp. 593–600.
- SAC-2001-KallesK #design #game studies #learning #on the #using #verification
- On verifying game designs and playing strategies using reinforcement learning (DK, PK), pp. 6–11.
- ITiCSE-2000-Sooriamurthi #abstraction #functional #recursion #using
- Using recursion as a tool to reinforce functional abstraction (poster session) (RS), p. 194.
- ICEIS-2000-KleinerSB #estimation #learning
- Self Organizing Maps for Value Estimation to Solve Reinforcement Learning Tasks (AK, BS, OB), pp. 149–156.
- CIKM-2000-Leuski #interactive
- Relevance and Reinforcement in Interactive Browsing (AL), pp. 119–126.
- ICML-2000-BaxterB #learning
- Reinforcement Learning in POMDP’s via Direct Gradient Ascent (JB, PLB), pp. 41–48.
- ICML-2000-Bowling #convergence #learning #multi #problem
- Convergence Problems of General-Sum Multiagent Reinforcement Learning (MHB), pp. 89–94.
- ICML-2000-DeJong #empirical #learning
- Hidden Strengths and Limitations: An Empirical Investigation of Reinforcement Learning (GD), pp. 215–222.
- ICML-2000-HougenGS #approach #learning
- An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control (DFH, MLG, JRS), pp. 383–390.
- ICML-2000-LagoudakisL #algorithm #learning #using
- Algorithm Selection using Reinforcement Learning (MGL, MLL), pp. 511–518.
- ICML-2000-LauerR #algorithm #distributed #learning #multi
- An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems (ML, MAR), pp. 535–542.
- ICML-2000-MorimotoD #behaviour #learning #using
- Acquisition of Stand-up Behavior by a Real Robot using Hierarchical Reinforcement Learning (JM, KD), pp. 623–630.
- ICML-2000-NgR #algorithm #learning
- Algorithms for Inverse Reinforcement Learning (AYN, SJR), pp. 663–670.
- ICML-2000-Randlov #learning #physics #problem
- Shaping in Reinforcement Learning by Changing the Physics of the Problem (JR), pp. 767–774.
- ICML-2000-RandlovBR #algorithm #learning
- Combining Reinforcement Learning with a Local Control Algorithm (JR, AGB, MTR), pp. 775–782.
- ICML-2000-Reynolds #adaptation #bound #clustering #learning
- Adaptive Resolution Model-Free Reinforcement Learning: Decision Boundary Partitioning (SIR), pp. 783–790.
- ICML-2000-RichterS #learning #modelling
- Knowledge Propagation in Model-based Reinforcement Learning Tasks (CR, JS), pp. 791–798.
- ICML-2000-RyanR #learning
- Learning to Fly: An Application of Hierarchical Reinforcement Learning (MRKR, MDR), pp. 807–814.
- ICML-2000-SmartK #learning
- Practical Reinforcement Learning in Continuous Spaces (WDS, LPK), pp. 903–910.
- ICML-2000-Strens #framework #learning
- A Bayesian Framework for Reinforcement Learning (MJAS), pp. 943–950.
- ICML-2000-TellerV #evolution #learning #performance #programming
- Efficient Learning Through Evolution: Neural Programming and Internal Reinforcement (AT, MMV), pp. 959–966.
- ICML-2000-Wiering #multi
- Multi-Agent Reinforcement Leraning for Traffic Light Control (MW), pp. 1151–1158.
- HCI-EI-1999-TanoT #adaptation #learning #user interface
- User Adaptation of the Pen-based User Interface by Reinforcement Learning (ST, MT), pp. 233–237.
- ICML-1999-AbeL #concept #learning #linear #probability #using
- Associative Reinforcement Learning using Linear Probabilistic Concepts (NA, PML), pp. 3–11.
- ICML-1999-PriceB #learning #multi
- Implicit Imitation in Multiagent Reinforcement Learning (BP, CB), pp. 325–334.
- ICML-1999-RennieM #learning #using #web
- Using Reinforcement Learning to Spider the Web Efficiently (JR, AM), pp. 335–343.
- ICLP-1999-SatoF #learning #logic programming
- Reactive Logic Programming by Reinforcement Learning (TS, SF), p. 617.
- ICML-1998-Dietterich #learning
- The MAXQ Method for Hierarchical Reinforcement Learning (TGD), pp. 118–126.
- ICML-1998-DzeroskiRB #learning #relational
- Relational Reinforcement Learning (SD, LDR, HB), pp. 136–143.
- ICML-1998-GaborKS #learning #multi
- Multi-criteria Reinforcement Learning (ZG, ZK, CS), pp. 197–205.
- ICML-1998-GarciaN #algorithm #analysis #learning
- A Learning Rate Analysis of Reinforcement Learning Algorithms in Finite-Horizon (FG, SMN), pp. 215–223.
- ICML-1998-HuW #algorithm #framework #learning #multi
- Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm (JH, MPW), pp. 242–250.
- ICML-1998-KearnsS #learning
- Near-Optimal Reinforcement Learning in Polynominal Time (MJK, SPS), pp. 260–268.
- ICML-1998-KimuraK #algorithm #analysis #learning #using
- An Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value Function (HK, SK), pp. 278–286.
- ICML-1998-PendrithM #analysis #learning #markov
- An Analysis of Direct Reinforcement Learning in Non-Markovian Domains (MDP, MM), pp. 421–429.
- ICML-1998-RandlovA #learning #using
- Learning to Drive a Bicycle Using Reinforcement Learning and Shaping (JR, PA), pp. 463–471.
- ICML-1998-RyanP #architecture #composition #learning #named
- RL-TOPS: An Architecture for Modularity and Re-Use in Reinforcement Learning (MRKR, MDP), pp. 481–487.
- ICPR-1998-PengB #learning #recognition
- Local reinforcement learning for object recognition (JP, BB), pp. 272–274.
- KDD-1998-MoodyS #learning
- Reinforcement Learning for Trading Systems and Portfolios (JEM, MS), pp. 279–283.
- ICML-1997-Fiechter #bound #learning #online
- Expected Mistake Bound Model for On-Line Reinforcement Learning (CNF), pp. 116–124.
- ICML-1997-KimuraMK #approximate #learning
- Reinforcement Learning in POMDPs with Function Approximation (HK, KM, SK), pp. 152–160.
- ICML-1997-PrecupS #learning
- Exponentiated Gradient Methods for Reinforcement Learning (DP, RSS), pp. 272–277.
- ICML-1997-TadepalliD #learning
- Hierarchical Explanation-Based Reinforcement Learning (PT, TGD), pp. 358–366.
- ICML-1996-GoetzKM #adaptation #learning #online
- On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning (PG, SK, RM), pp. 175–181.
- ICML-1996-LittmanS #convergence
- A Generalized Reinforcement-Learning Model: Convergence and Applications (MLL, CS), pp. 310–318.
- ICML-1996-Mahadevan #learning
- Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning (SM), pp. 328–336.
- ICML-1996-Moore #learning
- Reinforcement Learning in Factories: The Auton Project (Abstract) (AWM0), p. 556.
- ICML-1996-Munos #algorithm #convergence #learning
- A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning (RM), pp. 337–345.
- ICML-1996-PendrithR #difference #learning
- Actual Return Reinforcement Learning versus Temporal Differences: Some Theoretical and Experimental Results (MDP, MRKR), pp. 373–381.
- ICML-1996-TadepalliO #approximate #domain model #learning #modelling #scalability
- Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function (PT, DO), pp. 471–479.
- ICPR-1996-PengB #learning #recognition
- Delayed reinforcement learning for closed-loop object recognition (JP, BB), pp. 310–314.
- CSEE-1995-DickJ #education #industrial #learning
- Industry Involvement in Undergraduate Curricula: Reinforcing Learning by Applying the Principles (GND, SFJ), pp. 51–63.
- ICML-1995-Baird #algorithm #approximate #learning
- Residual Algorithms: Reinforcement Learning with Function Approximation (LCBI), pp. 30–37.
- ICML-1995-CichoszM #difference #learning #performance
- Fast and Efficient Reinforcement Learning with Truncated Temporal Differences (PC, JJM), pp. 99–107.
- ICML-1995-DietterichF #learning #perspective
- Explanation-Based Learning and Reinforcement Learning: A Unified View (TGD, NSF), pp. 176–184.
- ICML-1995-GambardellaD #approach #learning #named #problem
- Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem (LMG, MD), pp. 252–260.
- ICML-1995-KimuraYK #learning #probability
- Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward (HK, MY, SK), pp. 295–303.
- ICML-1995-McCallum #learning
- Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State (AM), pp. 387–395.
- ICML-1994-Littman #framework #game studies #learning #markov #multi
- Markov Games as a Framework for Multi-Agent Reinforcement Learning (MLL), pp. 157–163.
- ICML-1994-Mahadevan #case study #learning
- To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning (SM), pp. 164–172.
- DAC-1993-LewisP
- A Negative Reinforcement Method for PGA Routing (FDL, WCCP), pp. 601–605.
- ICML-1993-Lin #learning #scalability
- Scaling Up Reinforcement Learning for Robot Control (LJL), pp. 182–189.
- ICML-1993-Schwartz #learning
- A Reinforcement Learning Method for Maximizing Undiscounted Rewards (AS), pp. 298–305.
- ICML-1993-Tan #independence #learning #multi
- Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents (MT), pp. 330–337.
- ML-1992-ClouseU #education #learning
- A Teaching Method for Reinforcement Learning (JAC, PEU), pp. 92–110.
- ML-1992-Mahadevan #learning #modelling #probability
- Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions (SM), pp. 290–299.
- ML-1992-McCallum #learning #performance #proximity #using
- Using Transitional Proximity for Faster Reinforcement Learning (AM), pp. 316–321.
- ML-1992-Singh #algorithm #learning #modelling #scalability
- Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models (SPS), pp. 406–415.
- ML-1991-Berenji #approximate #learning #refinement
- Refinement of Approximate Reasoning-based Controllers by Reinforcement Learning (HRB), pp. 475–479.
- ML-1991-Lin #education #learning #self
- Self-improvement Based on Reinforcement Learning, Planning and Teaching (LJL), pp. 323–327.
- ML-1991-MahadevanC #architecture #learning #scalability
- Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture (SM, JC), pp. 328–332.
- ML-1991-MillanT #learning
- Learning to Avoid Obstacles Through Reinforcement (JdRM, CT), pp. 298–302.
- ML-1991-Tan #learning #representation
- Learning a Cost-Sensitive Internal Representation for Reinforcement Learning (MT), pp. 358–362.
- ML-1991-Wixson #composition #learning #scalability
- Scaling Reinforcement Learning Techniques via Modularity (LEW), pp. 3368–372.
- ML-1990-Kaelbling #learning
- Learning Functions in k-DNF from Reinforcement (LPK), pp. 162–169.
- ML-1990-WhiteheadB #learning
- Active Perception and Reinforcement Learning (SDW, DHB), pp. 179–188.
- ML-1988-Lynne #learning
- Competitive Reinforcement Learning (KJL), pp. 188–199.