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

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Showing 9576–9600 of 15113 papers

TitleStatusHype
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization—0
Deep Reinforcement Learning for Adaptive Traffic Signal Control—0
Deep Reinforcement Learning for Adaptive Learning Systems—0
Deep Reinforcement Learning for Adaptive Mesh Refinement—0
Deep Reinforcement Learning for Asset Allocation in US Equities—0
Deep Reinforcement Learning for Asset Allocation: Reward Clipping—0
Deep reinforcement learning for automatic run-time adaptation of UWB PHY radio settings—0
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges—0
Deep Reinforcement Learning for Autonomous Driving: A Survey—0
Deep Reinforcement Learning for Backup Strategies against Adversaries—0
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards—0
Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey—0
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control—0
Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks—0
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems—0
Deep Reinforcement Learning for Community Battery Scheduling under Uncertainties of Load, PV Generation, and Energy Prices—0
Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory—0
Deep Reinforcement Learning for Complex Manipulation Tasks with Sparse Feedback—0
Deep Reinforcement Learning for Constrained Field Development Optimization in Subsurface Two-phase Flow—0
Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitives—0
Deep Reinforcement Learning for Continuous Docking Control of Autonomous Underwater Vehicles: A Benchmarking Study—0
Deep Reinforcement Learning for Cyber Security—0
Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review—0
Deep Reinforcement Learning for Cyber System Defense under Dynamic Adversarial Uncertainties—0
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Benchmark Results

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