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 1726–1750 of 15113 papers

TitleStatusHype
TCR-GPT: Integrating Autoregressive Model and Reinforcement Learning for T-Cell Receptor Repertoires Generation—0
Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems—0
A Survey on Self-play Methods in Reinforcement Learning—0
Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment—0
On the Perturbed States for Transformed Input-robust Reinforcement LearningCode0
Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks—0
Multi-agent Assessment with QoS Enhancement for HD Map Updates in a Vehicular Network—0
ProSpec RL: Plan Ahead, then Execute—0
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations—0
Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems—0
Evolution of cooperation in the public goods game with Q-learning—0
A Method for Fast Autonomy Transfer in Reinforcement Learning—0
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Appraisal-Guided Proximal Policy Optimization: Modeling Psychological Disorders in Dynamic Grid World—0
Anomalous State Sequence Modeling to Enhance Safety in Reinforcement Learning—0
NAVIX: Scaling MiniGrid Environments with JAXCode2
Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers—0
QT-TDM: Planning With Transformer Dynamics Model and Autoregressive Q-Learning—0
Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning—0
Pretrained Visual Representations in Reinforcement Learning—0
Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems—0
Gymnasium: A Standard Interface for Reinforcement Learning EnvironmentsCode11
Path Following and Stabilisation of a Bicycle Model using a Reinforcement Learning Approach—0
SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning—0
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement LearningCode2
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Benchmark Results

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