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 4651–4700 of 15113 papers

TitleStatusHype
B-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis—0
Multi-Agent Reinforcement Learning for Power Grid Topology OptimizationCode0
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making—0
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own—0
Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiencyCode0
Blending Imitation and Reinforcement Learning for Robust Policy Improvement—0
A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback—0
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model—0
On Representation Complexity of Model-based and Model-free Reinforcement Learning—0
Prioritized Soft Q-Decomposition for Lexicographic Reinforcement LearningCode0
Navigating Uncertainty in ESG Investing—0
Towards a Unified Framework for Sequential Decision Making—0
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning—0
REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of primary sclerosing cholangitis DIsease progression—0
Improving Dialogue Management: Quality Datasets vs ModelsCode0
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information—0
Controlling Neural Style Transfer with Deep Reinforcement Learning—0
Adaptive Control of an Inverted Pendulum by a Reinforcement Learning-based LQR Method—0
A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning—0
Learning to Rewrite Prompts for Personalized Text Generation—0
Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation—0
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill DiscoveryCode0
Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and SmoothnessCode0
Reinforcement Learning for Node Selection in Branch-and-Bound—0
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments—0
Robust Offline Reinforcement Learning -- Certify the Confidence Interval—0
Stackelberg Batch Policy Learning—0
Efficiency Separation between RL Methods: Model-Free, Model-Based and Goal-Conditioned—0
Raijū: Reinforcement Learning-Guided Post-Exploitation for Automating Security Assessment of Network Systems—0
PlotMap: Automated Layout Design for Building Game WorldsCode0
Tempo Adaptation in Non-stationary Reinforcement LearningCode0
ODE-based Recurrent Model-free Reinforcement Learning for POMDPs—0
Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning—0
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds—0
On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors—0
A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies—0
Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills—0
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based RolloutCode0
Iterative Reachability Estimation for Safe Reinforcement Learning—0
Limits of Actor-Critic Algorithms for Decision Tree Policies Learning in IBMDPs—0
Reinforcement Learning for Robust Header Compression under Model Uncertainty—0
Offline to Online Learning for Real-Time Bandwidth Estimation—0
Robotic Offline RL from Internet Videos via Value-Function Pre-Training—0
H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps—0
Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution—0
Delays in Reinforcement Learning—0
Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces—0
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward—0
Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rules—0
Privileged to Predicted: Towards Sensorimotor Reinforcement Learning for Urban Driving—0
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Benchmark Results

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