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 1226–1250 of 15113 papers

TitleStatusHype
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster trainingCode1
Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy OptimizationCode1
Causal Influence Detection for Improving Efficiency in Reinforcement LearningCode1
Fully Decentralized Multi-Agent Reinforcement Learning with Networked AgentsCode1
Functional Regularization for Reinforcement Learning via Learned Fourier FeaturesCode1
ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement LearningCode1
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement LearningCode1
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTSCode1
CDT: Cascading Decision Trees for Explainable Reinforcement LearningCode1
A Policy-Guided Imitation Approach for Offline Reinforcement LearningCode1
Gamma and Vega Hedging Using Deep Distributional Reinforcement LearningCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Cell-Free Latent Go-ExploreCode1
Certified Reinforcement Learning with Logic GuidanceCode1
Generalising Discrete Action Spaces with Conditional Action TreesCode1
CertRL: Formalizing Convergence Proofs for Value and Policy Iteration in CoqCode1
CFR-RL: Traffic Engineering with Reinforcement Learning in SDNCode1
Action Space Shaping in Deep Reinforcement LearningCode1
Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward EnvironmentsCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
Generalization in Reinforcement Learning by Soft Data AugmentationCode1
Visual Grounding for Object-Level Generalization in Reinforcement LearningCode1
Energy Pricing in P2P Energy Systems Using Reinforcement LearningCode1
Generalizing Across Multi-Objective Reward Functions in Deep Reinforcement LearningCode1
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their SolutionsCode1
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Benchmark Results

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