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 4476–4500 of 15113 papers

TitleStatusHype
AI-based Radio Resource Management and Trajectory Design for PD-NOMA Communication in IRS-UAV Assisted Networks—0
AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments—0
AI-based Robust Resource Allocation in End-to-End Network Slicing under Demand and CSI Uncertainties—0
AI-based traffic analysis in digital twin networks—0
AI-driven materials design: a mini-review—0
AI-Driven Resource Allocation Framework for Microservices in Hybrid Cloud Platforms—0
AI-Driven Resource Allocation in Optical Wireless Communication Systems—0
AIGB: Generative Auto-bidding via Conditional Diffusion Modeling—0
AIGenC: An AI generalisation model via creativity—0
AI Planning: A Primer and Survey (Preliminary Report)—0
AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning—0
AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning—0
AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference—0
AISYN: AI-driven Reinforcement Learning-Based Logic Synthesis Framework—0
AITuning: Machine Learning-based Tuning Tool for Run-Time Communication Libraries—0
Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels—0
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces—0
A Language Model based Evaluator for Sentence Compression—0
A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning—0
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning—0
A Learned Simulation Environment to Model Plant Growth in Indoor Farming—0
A Learned Simulation Environment to Model Student Engagement and Retention in Automated Online Courses—0
A Learning based Branch and Bound for Maximum Common Subgraph Problems—0
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning—0
A Learning Framework for High Precision Industrial Assembly—0
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Benchmark Results

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