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 1326–1350 of 15113 papers

TitleStatusHype
An Open-source Sim2Real Approach for Sensor-independent Robot Navigation in a GridCode0
Pre-trained Visual Dynamics Representations for Efficient Policy Learning—0
Embedding Safety into RL: A New Take on Trust Region Methods—0
When to Localize? A Risk-Constrained Reinforcement Learning Approach—0
Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation—0
N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs—0
Risk-sensitive control as inference with Rényi divergenceCode0
Show, Don't Tell: Learning Reward Machines from Demonstrations for Reinforcement Learning-Based Cardiac Pacemaker Synthesis—0
Simulation of Nanorobots with Artificial Intelligence and Reinforcement Learning for Advanced Cancer Cell Detection and TrackingCode0
So You Think You Can Scale Up Autonomous Robot Data Collection?—0
Diversity Progress for Goal Selection in Discriminability-Motivated RL—0
GITSR: Graph Interaction Transformer-based Scene Representation for Multi Vehicle Collaborative Decision-making—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
A Review of Reinforcement Learning in Financial Applications—0
Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation—0
AI-based traffic analysis in digital twin networks—0
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory—0
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data CorruptionsCode0
Effective ML Model Versioning in Edge Networks—0
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization—0
Scalable Reinforcement Post-Training Beyond Static Human Prompts: Evolving Alignment via Asymmetric Self-Play—0
Maximum Entropy Hindsight Experience Replay—0
Deterministic Exploration via Stationary Bellman Error Maximization—0
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Benchmark Results

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