SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 13051–13100 of 15113 papers

TitleStatusHype
Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement LearningCode0
The Game of Tetris in Machine LearningCode0
Continuous Action Reinforcement Learning from a Mixture of Interpretable ExpertsCode0
Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware ImpairmentsCode0
Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy EvaluationCode0
Reinforcement Learning for Uplift ModelingCode0
Policy Constraint by Only Support Constraint for Offline Reinforcement LearningCode0
Multi-view Disentanglement for Reinforcement Learning with Multiple CamerasCode0
Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement LearningCode0
Variational Delayed Policy OptimizationCode0
Reinforcement Learning for Topic ModelsCode0
SOLAR: Deep Structured Representations for Model-Based Reinforcement LearningCode0
RLFlow: Optimising Neural Network Subgraph Transformation with World ModelsCode0
Off-Policy Actor-CriticCode0
RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape CompletionCode0
Offline Safe Reinforcement Learning Using Trajectory ClassificationCode0
RLgraph: Modular Computation Graphs for Deep Reinforcement LearningCode0
VQC-Based Reinforcement Learning with Data Re-uploading: Performance and TrainabilityCode0
Training Adversarial Agents to Exploit Weaknesses in Deep Control PoliciesCode0
Training Agents using Upside-Down Reinforcement LearningCode0
Multivariate Time Series Early Classification Across Channel and Time DimensionsCode0
Lusifer: LLM-based User SImulated Feedback Environment for online Recommender systemsCode0
Reinforcement Learning for Solving the Vehicle Routing ProblemCode0
Training an Interactive Humanoid Robot Using Multimodal Deep Reinforcement LearningCode0
Reinforcement Learning for Solving Stochastic Vehicle Routing ProblemCode0
Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal LogicCode0
Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov ModelCode0
Reinforcement Learning for Robotic Manipulation using Simulated Locomotion DemonstrationsCode0
Policy Consolidation for Continual Reinforcement LearningCode0
Solving Common-Payoff Games with Approximate Policy IterationCode0
Policy Augmentation: An Exploration Strategy for Faster Convergence of Deep Reinforcement Learning AlgorithmsCode0
Variational Generative Stochastic Networks with Collaborative ShapingCode0
RL-NCS: Reinforcement learning based data-driven approach for nonuniform compressed sensingCode0
Multi-timescale memory dynamics in a reinforcement learning network with attention-gated memoryCode0
POCE: Primal Policy Optimization with Conservative Estimation for Multi-constraint Offline Reinforcement LearningCode0
Reinforcement learning for Quantum Tiq-Taq-ToeCode0
PlotMap: Automated Layout Design for Building Game WorldsCode0
Reinforcement Learning for Portfolio ManagementCode0
Reinforcement Learning for Pivoting TaskCode0
Offline RL with Smooth OOD Generalization in Convex Hull and its NeighborhoodCode0
RL-PGO: Reinforcement Learning-based Planar Pose-Graph OptimizationCode0
Playing Text-Adventure Games with Graph-Based Deep Reinforcement LearningCode0
RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled PlatformsCode0
Reinforcement Learning for Physical Layer CommunicationsCode0
Reinforcement learning for multi-item retrieval in the puzzle-based storage systemCode0
Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy OptimizationCode0
Modern Deep Reinforcement Learning AlgorithmsCode0
Reinforcement Learning for Market Making in a Multi-agent Dealer MarketCode0
Offline Reinforcement Learning from Datasets with Structured Non-StationarityCode0
RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified