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 6376–6400 of 15113 papers

TitleStatusHype
Reinforcement Learning Your Way: Agent Characterization through Policy Regularization—0
Reinforcement Learning for Personalized Drug Discovery and Design for Complex Diseases: A Systems Pharmacology Perspective—0
Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search—0
Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement LearningCode0
Environment Generation for Zero-Shot Compositional Reinforcement Learning—0
Deep Reinforcement Learning with Spiking Q-learning—0
Deep reinforcement learning under signal temporal logic constraints using Lagrangian relaxation—0
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning—0
Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning—0
DROPO: Sim-to-Real Transfer with Offline Domain RandomizationCode1
A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning—0
Goal-Conditioned Reinforcement Learning: Problems and SolutionsCode1
Self-Awareness Safety of Deep Reinforcement Learning in Road Traffic Junction Driving—0
Addressing Maximization Bias in Reinforcement Learning with Two-Sample TestingCode1
Safety-Aware Multi-Agent Apprenticeship Learning—0
Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning—0
Learning Multi-agent Skills for Tabular Reinforcement Learning using Factor Graphs—0
Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees—0
Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning—0
Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams—0
Reinforcement Learning TextbookCode2
Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation—0
Anytime PSRO for Two-Player Zero-Sum Games—0
Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections—0
Conservative Distributional Reinforcement Learning with Safety Constraints—0
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Benchmark Results

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