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 2901–2925 of 15113 papers

TitleStatusHype
Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality—0
AI-driven materials design: a mini-review—0
Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks—0
Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems—0
AAMDRL: Augmented Asset Management with Deep Reinforcement Learning—0
Attention-based Deep Reinforcement Learning for Multi-view Environments—0
AI-based traffic analysis in digital twin networks—0
Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States—0
Decentralized Motor Skill Learning for Complex Robotic Systems—0
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances—0
Decentralized Multi-Robot Formation Control Using Reinforcement Learning—0
Decentralized RL-Based Data Transmission Scheme for Energy Efficient Harvesting—0
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making—0
Attention-Aware Face Hallucination via Deep Reinforcement Learning—0
Attention-Aware Deep Reinforcement Learning for Video Face Recognition—0
AI-based Robust Resource Allocation in End-to-End Network Slicing under Demand and CSI Uncertainties—0
Attentional Policies for Cross-Context Multi-Agent Reinforcement Learning—0
AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments—0
AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control—0
Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention—0
AI-based Radio Resource Management and Trajectory Design for PD-NOMA Communication in IRS-UAV Assisted Networks—0
Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehicles—0
AI Assisted Annotator using Reinforcement Learning—0
Adaptive Batch Size for Safe Policy Gradients—0
Attacking and Defending Deep Reinforcement Learning Policies—0
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Benchmark Results

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