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 3601–3625 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography—0
Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes—0
Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey—0
Automatic Text Summarization Using Reinforcement Learning with Embedding Features—0
Deep Reinforcement Learning for DER Cyber-Attack Mitigation—0
Deep Reinforcement Learning for Detecting Malicious Websites—0
Cost-Sensitive Exploration in Bayesian Reinforcement Learning—0
Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks—0
Deep Reinforcement Learning for Distributed Uncoordinated Cognitive Radios Resource Allocation—0
Deep Reinforcement Learning for Distributed and Uncoordinated Cognitive Radios Resource Allocation—0
A State Representation for Diminishing Rewards—0
Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data—0
Deep Reinforcement Learning for Dynamic Spectrum Sensing and Aggregation in Multi-Channel Wireless Networks—0
Deep Reinforcement Learning for Dynamic Spectrum Sharing of LTE and NR—0
CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning—0
Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks—0
A State Representation Dueling Network for Deep Reinforcement Learning—0
Deep Reinforcement Learning for Dynamic Urban Transportation Problems—0
Agent-Agnostic Human-in-the-Loop Reinforcement Learning—0
Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows—0
Deep Reinforcement Learning for Entity Alignment—0
Deterministic Value-Policy Gradients—0
Deep Reinforcement Learning for Equal Risk Pricing and Hedging under Dynamic Expectile Risk Measures—0
Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning—0
A State Augmentation based approach to Reinforcement Learning from Human Preferences—0
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Benchmark Results

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