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.