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 9001–9025 of 15113 papers

TitleStatusHype
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
Reinforcement learning based local search for grouping problems: A case study on graph coloring—0
Reinforcement Learning based Multi-connectivity Resource Allocation in Factory Automation Systems—0
Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction—0
Reinforcement Learning based on MPC/MHE for Unmodeled and Partially Observable Dynamics—0
Reinforcement Learning Based on Real-Time Iteration NMPC—0
Reinforcement Learning based on Scenario-tree MPC for ASVs—0
Reinforcement Learning Based Optimal Camera Placement for Depth Observation of Indoor Scenes—0
Deterministic policy gradient based optimal control with probabilistic constraints—0
Reinforcement Learning-based Optimal Control and Software Rejuvenation for Safe and Efficient UAV Navigation—0
Reinforcement learning-based optimised control for tracking of nonlinear systems with adversarial attacks—0
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Benchmark Results

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