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 1491–1500 of 15113 papers

TitleStatusHype
GAEA: Graph Augmentation for Equitable Access via Reinforcement LearningCode1
Deep Reinforcement Learning for Turbulence Modeling in Large Eddy SimulationsCode1
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly DataCode1
Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB trafficCode1
Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning StudyCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Deep Reinforcement Learning from Self-Play in Imperfect-Information GamesCode1
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning TechniquesCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
First return, then exploreCode1
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Benchmark Results

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