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 6301–6325 of 15113 papers

TitleStatusHype
Successor Options : An Option Discovery Algorithm for Reinforcement Learning—0
Success-Rate Targeted Reinforcement Learning by Disorientation Penalty—0
SUMBT+LaRL: Effective Multi-domain End-to-end Neural Task-oriented Dialog System—0
Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction—0
SUMO: Search-Based Uncertainty Estimation for Model-Based Offline Reinforcement Learning—0
Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning—0
Superior Computer Chess with Model Predictive Control, Reinforcement Learning, and Rollout—0
Superior Performance with Diversified Strategic Control in FPS Games Using General Reinforcement Learning—0
Superkernel Neural Architecture Search for Image Denoising—0
SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation—0
Superstition in the Network: Deep Reinforcement Learning Plays Deceptive Games—0
Supervised Advantage Actor-Critic for Recommender Systems—0
Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess—0
Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)—0
Supervised Pretraining Can Learn In-Context Reinforcement Learning—0
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation—0
SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning—0
Supplementing Gradient-Based Reinforcement Learning with Simple Evolutionary Ideas—0
SURF: Semantic-level Unsupervised Reward Function for Machine Translation—0
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning—0
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning—0
SURREAL-System: Fully-Integrated Stack for Distributed Deep Reinforcement Learning—0
Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network—0
Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning—0
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles—0
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Benchmark Results

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