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 3801–3825 of 15113 papers

TitleStatusHype
Oralytics Reinforcement Learning AlgorithmCode0
Learned Graph Rewriting with Equality Saturation: A New Paradigm in Relational Query Rewrite and Beyond—0
Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols—0
Order-Optimal Instance-Dependent Bounds for Offline Reinforcement Learning with Preference Feedback—0
Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning—0
More Efficient Randomized Exploration for Reinforcement Learning via Approximate SamplingCode0
A Systematization of the Wagner Framework: Graph Theory Conjectures and Reinforcement LearningCode0
Quantum Compiling with Reinforcement Learning on a Superconducting Processor—0
Physics-informed Imitative Reinforcement Learning for Real-world Driving—0
Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection—0
Constructing Ancestral Recombination Graphs through Reinforcement Learning—0
Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms—0
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions—0
Adding Conditional Control to Diffusion Models with Reinforcement Learning—0
Design of Interacting Particle Systems for Fast Linear Quadratic RL—0
UniZero: Generalized and Efficient Planning with Scalable Latent World Models—0
Generating and Evolving Reward Functions for Highway Driving with Large Language Models—0
Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models—0
ROAR: Reinforcing Original to Augmented Data Ratio Dynamics for Wav2Vec2.0 Based ASR—0
Finite-Time Analysis of Simultaneous Double Q-learning—0
Adaptive Actor-Critic Based Optimal Regulation for Drift-Free Uncertain Nonlinear Systems—0
Data-driven modeling and supervisory control system optimization for plug-in hybrid electric vehicles—0
SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets—0
e-COP : Episodic Constrained Optimization of Policies—0
CIMRL: Combining IMitation and Reinforcement Learning for Safe Autonomous Driving—0
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Benchmark Results

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