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 1151–1175 of 15113 papers

TitleStatusHype
Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models—0
Harvesting energy from turbulent winds with Reinforcement Learning—0
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous InferenceCode1
CLIP-RLDrive: Human-Aligned Autonomous Driving via CLIP-Based Reward Shaping in Reinforcement Learning—0
Guiding Generative Protein Language Models with Reinforcement LearningCode2
Design of Restricted Normalizing Flow towards Arbitrary Stochastic Policy with Computational Efficiency—0
ParMod: A Parallel and Modular Framework for Learning Non-Markovian Tasks—0
Learning Visuotactile Estimation and Control for Non-prehensile Manipulation under Occlusions—0
Multi-Task Reinforcement Learning for Quadrotors—0
Tilted Quantile Gradient Updates for Quantile-Constrained Reinforcement LearningCode0
Equivariant Action Sampling for Reinforcement Learning and Planning—0
Using machine learning to inform harvest control rule design in complex fishery settingsCode0
MGDA: Model-based Goal Data Augmentation for Offline Goal-conditioned Weighted Supervised Learning—0
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization—0
Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation—0
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents—0
RL-LLM-DT: An Automatic Decision Tree Generation Method Based on RL Evaluation and LLM EnhancementCode1
Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement LearningCode1
Are Expressive Models Truly Necessary for Offline RL?Code1
Entropy-Regularized Process Reward ModelCode1
Adaptive Reward Design for Reinforcement LearningCode0
Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement—0
Continuous-time optimal investment with portfolio constraints: a reinforcement learning approach—0
Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection—0
Reward Machine Inference for Robotic Manipulation—0
Show:102550
← PrevPage 47 of 605Next →

Benchmark Results

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