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 9526–9550 of 15113 papers

TitleStatusHype
Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion—0
Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics—0
Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center—0
Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation—0
Reinforced dynamics for enhanced sampling in large atomic and molecular systems—0
Reinforced Extractive Summarization with Question-Focused Rewards—0
Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem—0
Reinforced Imitation in Heterogeneous Action Space—0
Reinforced Imitation Learning by Free Energy Principle—0
Reinforced Inverse Scattering—0
Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement—0
Reinforced Latent Reasoning for LLM-based Recommendation—0
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT—0
Deep Reinforcement Learning Attention Selection for Person Re-Identification—0
Deep Reinforcement Learning Based Optimization for IRS Based UAV-NOMA Downlink Networks—0
Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications—0
Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense—0
Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks—0
Deep Reinforcement Learning-based Anti-jamming Power Allocation in a Two-cell NOMA Network—0
Deep Reinforcement Learning-based Authentic Dialogue Generation To Protect Youth From Cybergrooming—0
Deep Reinforcement Learning-Based Beam Tracking for Low-Latency Services in Vehicular Networks—0
Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment—0
Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks—0
Deep Reinforcement Learning Based Controller for Active Heave Compensation—0
Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing—0
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Benchmark Results

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