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 10551–10575 of 15113 papers

TitleStatusHype
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
Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training—0
Survey on Fair Reinforcement Learning: Theory and Practice—0
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods—0
Survey on Multi-Agent Q-Learning frameworks for resource management in wireless sensor network—0
Survey on reinforcement learning for language processing—0
Survey on Strategic Mining in Blockchain: A Reinforcement Learning Approach—0
Surveys without Questions: A Reinforcement Learning Approach—0
Survival Analysis on Structured Data using Deep Reinforcement Learning—0
Survival Instinct in Offline Reinforcement Learning—0
Survival of the Fittest: Evolutionary Adaptation of Policies for Environmental Shifts—0
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning—0
SVQN: Sequential Variational Soft Q-Learning Networks—0
Swarm Behavior Cloning—0
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Benchmark Results

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