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 3376–3400 of 15113 papers

TitleStatusHype
Lagrangian-based online safe reinforcement learning for state-constrained systems—0
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and PracticeCode0
Policy Representation via Diffusion Probability Model for Reinforcement LearningCode1
INVICTUS: Optimizing Boolean Logic Circuit Synthesis via Synergistic Learning and Search—0
Offline Primal-Dual Reinforcement Learning for Linear MDPs—0
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations—0
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex ManipulationCode2
Towards Optimal Energy Management Strategy for Hybrid Electric Vehicle with Reinforcement Learning—0
BertRLFuzzer: A BERT and Reinforcement Learning Based FuzzerCode0
SneakyPrompt: Jailbreaking Text-to-image Generative ModelsCode1
Model-based adaptation for sample efficient transfer in reinforcement learning control of parameter-varying systems—0
Understanding the World to Solve Social Dilemmas Using Multi-Agent Reinforcement Learning—0
Learning Diverse Risk Preferences in Population-based Self-playCode1
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond—0
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
Client Selection for Federated Policy Optimization with Environment HeterogeneityCode0
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL—0
Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional CurriculumCode1
A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning PoliciesCode0
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning—0
Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCSCode0
Revisiting the Minimalist Approach to Offline Reinforcement LearningCode1
Cooperation Is All You Need—0
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions—0
Coagent Networks: Generalized and Scaled—0
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Benchmark Results

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