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 5276–5300 of 15113 papers

TitleStatusHype
Modular Lifelong Reinforcement Learning via Neural CompositionCode1
Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning—0
On the Learning and Learnability of QuasimetricsCode1
Performative Reinforcement Learning—0
Denoised MDPs: Learning World Models Better Than the World ItselfCode1
Deep Reinforcement Learning with Swin TransformersCode0
Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles—0
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning—0
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization—0
Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations—0
Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement LearningCode0
Minimalist and High-performance Conversational Recommendation with Uncertainty Estimation for User Preference—0
Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting—0
Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions—0
Short-Term Plasticity Neurons Learning to Learn and ForgetCode1
Masked World Models for Visual Control—0
Risk Perspective Exploration in Distributional Reinforcement Learning—0
Traffic Management of Autonomous Vehicles using Policy Based Deep Reinforcement Learning and Intelligent Routing—0
Spatial Positioning Token (SPToken) for Smart Parking—0
Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning—0
Low Emission Building Control with Zero-Shot Reinforcement LearningCode0
Dependency Parsing with Backtracking using Deep Reinforcement Learning—0
Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Network—0
DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning SystemsCode0
DayDreamer: World Models for Physical Robot LearningCode2
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Benchmark Results

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