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 1626–1650 of 15113 papers

TitleStatusHype
Gaussian-Mixture-Model Q-Functions for Reinforcement Learning by Riemannian Optimization—0
InfraLib: Enabling Reinforcement Learning and Decision-Making for Large-Scale Infrastructure Management—0
Differentiable Discrete Event Simulation for Queuing Network Control—0
CHIRPs: Change-Induced Regret Proxy metrics for Lifelong Reinforcement Learning—0
Robust synchronization and policy adaptation for networked heterogeneous agents—0
Reinforcement Learning Approach to Optimizing Profilometric Sensor Trajectories for Surface Inspection—0
ELO-Rated Sequence Rewards: Advancing Reinforcement Learning ModelsCode0
Enhancing Information Freshness: An AoI Optimized Markov Decision Process Dedicated In the Underwater Task—0
Continual Diffuser (CoD): Mastering Continual Offline Reinforcement Learning with Experience RehearsalCode0
Large Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement Learning—0
Tractable Offline Learning of Regular Decision Processes—0
State and Action Factorization in Power Grids—0
Reinforcement Learning-enabled Satellite Constellation Reconfiguration and Retasking for Mission-Critical Applications—0
Grounding Language Models in Autonomous Loco-manipulation Tasks—0
MOOSS: Mask-Enhanced Temporal Contrastive Learning for Smooth State Evolution in Visual Reinforcement LearningCode0
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy OptimizationCode2
Diffusion Policy Policy OptimizationCode4
AgGym: An agricultural biotic stress simulation environment for ultra-precision management planningCode0
Foundations of Multivariate Distributional Reinforcement Learning—0
Robust off-policy Reinforcement Learning via Soft Constrained Adversary—0
Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory controlCode1
Discovery of False Data Injection Schemes on Frequency Controllers with Reinforcement Learning—0
AdapShare: An RL-Based Dynamic Spectrum Sharing Solution for O-RAN—0
On Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes—0
RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate ModelsCode0
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Benchmark Results

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