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 876–900 of 15113 papers

TitleStatusHype
MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid DynamicsCode1
Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task EnvironmentsCode1
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data FormatCode1
Efficient Reinforcement Learning Through Trajectory GenerationCode1
One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement LearningCode1
Real-time Bidding Strategy in Display Advertising: An Empirical AnalysisCode1
The Effectiveness of World Models for Continual Reinforcement LearningCode1
Improved Representation of Asymmetrical Distances with Interval Quasimetric EmbeddingsCode1
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage ProbabilityCode1
BEAR: Physics-Principled Building Environment for Control and Reinforcement LearningCode1
Masked Autoencoding for Scalable and Generalizable Decision MakingCode1
TEMPERA: Test-Time Prompting via Reinforcement LearningCode1
Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop SchedulingCode1
Efficient Meta Reinforcement Learning for Preference-based Fast AdaptationCode1
Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing FlowsCode1
Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value FunctionCode1
LibSignal: An Open Library for Traffic Signal ControlCode1
Language-Conditioned Reinforcement Learning to Solve Misunderstandings with Action CorrectionsCode1
Towards Data-Driven Offline Simulations for Online Reinforcement LearningCode1
Redeeming Intrinsic Rewards via Constrained OptimizationCode1
Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement LearningCode1
Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable ConceptsCode1
Curriculum-based Asymmetric Multi-task Reinforcement LearningCode1
Design Process is a Reinforcement Learning ProblemCode1
Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for RoboticsCode1
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Benchmark Results

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