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 9601–9625 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
Deep Reinforcement Learning for DER Cyber-Attack Mitigation—0
Deep Reinforcement Learning for Detecting Malicious Websites—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
Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks—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
Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation—0
Deep Reinforcement Learning for Dynamic Band Switch in Cellular-Connected UAV—0
Deep Reinforcement Learning for Dynamic Urban Transportation Problems—0
Deep Reinforcement Learning for Electric Transmission Voltage Control—0
Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows—0
Deep Reinforcement Learning for Entity Alignment—0
Deep Reinforcement Learning for Equal Risk Pricing and Hedging under Dynamic Expectile Risk Measures—0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Deep Reinforcement Learning for Field Development Optimization—0
Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder—0
Deep Reinforcement Learning for Foreign Exchange Trading—0
Domain-adapted Learning and Interpretability: DRL for Gas Trading—0
Deep Reinforcement Learning for Green Security Games with Real-Time Information—0
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Benchmark Results

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