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 3326–3350 of 15113 papers

TitleStatusHype
Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation—0
Pre-trained Visual Dynamics Representations for Efficient Policy Learning—0
When to Localize? A Risk-Constrained Reinforcement Learning Approach—0
Embedding Safety into RL: A New Take on Trust Region Methods—0
An Open-source Sim2Real Approach for Sensor-independent Robot Navigation in a GridCode0
Show, Don't Tell: Learning Reward Machines from Demonstrations for Reinforcement Learning-Based Cardiac Pacemaker Synthesis—0
N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs—0
Risk-sensitive control as inference with Rényi divergenceCode0
So You Think You Can Scale Up Autonomous Robot Data Collection?—0
Simulation of Nanorobots with Artificial Intelligence and Reinforcement Learning for Advanced Cancer Cell Detection and TrackingCode0
GITSR: Graph Interaction Transformer-based Scene Representation for Multi Vehicle Collaborative Decision-making—0
Diversity Progress for Goal Selection in Discriminability-Motivated RL—0
Hedging and Pricing Structured Products Featuring Multiple Underlying Assets—0
Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization—0
StepCountJITAI: simulation environment for RL with application to physical activity adaptive interventionCode0
AI-based traffic analysis in digital twin networks—0
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory—0
Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation—0
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data CorruptionsCode0
A Review of Reinforcement Learning in Financial Applications—0
Effective ML Model Versioning in Edge Networks—0
Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language UseCode0
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs—0
Noise as a Double-Edged Sword: Reinforcement Learning Exploits Randomized Defenses in Neural Networks—0
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization—0
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Benchmark Results

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