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

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Showing 1626–1650 of 15113 papers

TitleStatusHype
Model-based Constrained Reinforcement Learning using Generalized Control Barrier FunctionCode1
Latent Imagination Facilitates Zero-Shot Transfer in Autonomous RacingCode1
Model-Based Visual Planning with Self-Supervised Functional DistancesCode1
Model-free Deep Reinforcement Learning for Urban Autonomous DrivingCode1
ModelicaGym: Applying Reinforcement Learning to Modelica ModelsCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
MoDem: Accelerating Visual Model-Based Reinforcement Learning with DemonstrationsCode1
Modular Lifelong Reinforcement Learning via Neural CompositionCode1
A Text-based Deep Reinforcement Learning Framework for Interactive RecommendationCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
MOPO: Model-based Offline Policy OptimizationCode1
Motif: Intrinsic Motivation from Artificial Intelligence FeedbackCode1
MPCritic: A plug-and-play MPC architecture for reinforcement learningCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
An Empirical Study of Representation Learning for Reinforcement Learning in HealthcareCode1
Multi-Agent Car Parking using Reinforcement LearningCode1
Multi-Agent Constrained Policy OptimisationCode1
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement LearningCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
Multi-Agent Generative Adversarial Imitation LearningCode1
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement LearningCode1
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Benchmark Results

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