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 4261–4270 of 15113 papers

TitleStatusHype
A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming—0
A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games—0
A deep learning model for gas storage optimization—0
A Deep Neural Network Algorithm for Linear-Quadratic Portfolio Optimization with MGARCH and Small Transaction Costs—0
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise—0
A Deep Reinforcement Learning Approach towards Pendulum Swing-up Problem based on TF-Agents—0
A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization—0
A Deep Reinforcement Learning Approach for Composing Moving IoT Services—0
A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback—0
A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data—0
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Benchmark Results

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