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 4076–4100 of 15113 papers

TitleStatusHype
FGAIF: Aligning Large Vision-Language Models with Fine-grained AI Feedback—0
Compositional Conservatism: A Transductive Approach in Offline Reinforcement LearningCode0
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning—0
Continual Policy Distillation of Reinforcement Learning-based Controllers for Soft Robotic In-Hand ManipulationCode0
Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology—0
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning—0
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers—0
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm—0
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionCode0
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning—0
SliceIt! -- A Dual Simulator Framework for Learning Robot Food SlicingCode0
Reinforcement Learning in Categorical Cybernetics—0
Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation—0
Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning—0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning—0
Asymptotics of Language Model Alignment—0
MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control—0
Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning—0
Learning Off-policy with Model-based Intrinsic Motivation For Active Online Exploration—0
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods—0
Molecular Generative Adversarial Network with Multi-Property Optimization—0
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering—0
Learning Visual Quadrupedal Loco-Manipulation from Demonstrations—0
CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning—0
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Benchmark Results

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