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 4726–4750 of 15113 papers

TitleStatusHype
Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs—0
Reinforcement Learning Based Character Controlling—0
Reinforcement Learning-Based Controlled Switching Approach for Inrush Current Minimization in Power Transformers—0
Reinforcement Learning-based Control of Nonlinear Systems using Carleman Approximation: Structured and Unstructured Designs—0
Reinforcement Learning Based Cooperative Coded Caching under Dynamic Popularities in Ultra-Dense Networks—0
Reinforcement Learning-Based Cooperative P2P Power Trading between DC Nanogrid Clusters with Wind and PV Energy Resources—0
Reinforcement Learning-Based Coverage Path Planning with Implicit Cellular Decomposition—0
Reinforcement Learning based Curriculum Optimization for Neural Machine Translation—0
Reinforcement Learning-based Defect Mitigation for Quality Assurance of Additive Manufacturing—0
Reinforcement Learning based Design of Linear Fixed Structure Controllers—0
Reinforcement Learning based Disease Progression Model for Alzheimer's Disease—0
Reinforcement Learning based Disease Progression Model for Alzheimer’s Disease—0
Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting—0
Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting—0
Reinforcement Learning-based Dynamic Service Placement in Vehicular Networks—0
Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting—0
Reinforcement Learning based Embodied Agents Modelling Human Users Through Interaction and Multi-Sensory Perception—0
Reinforcement Learning Based Emotional Editing Constraint Conversation Generation—0
Reinforcement learning-based estimation for partial differential equations—0
Reinforcement Learning-based Fault-Tolerant Control for Quadrotor with Online Transformer Adaptation—0
Reinforcement Learning Based Handwritten Digit Recognition with Two-State Q-Learning—0
Reinforcement Learning-Based Heterogeneous Multi-Task Optimization in Semantic Broadcast Communications—0
Reinforcement Learning-based Joint Handover and Beam Tracking in Millimeter-wave Networks—0
Reinforcement Learning-based Joint Path and Energy Optimization of Cellular-Connected Unmanned Aerial Vehicles—0
Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking—0
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Benchmark Results

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