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 701–710 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Band Selection in Hyperspectral Image ClassificationCode1
Deep Reinforcement Learning for Adaptive Exploration of Unknown EnvironmentsCode1
Active MR k-space Sampling with Reinforcement LearningCode1
Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply ChainsCode1
Deep Reinforcement Learning for Computational Fluid Dynamics on HPC SystemsCode1
Asset Allocation: From Markowitz to Deep Reinforcement LearningCode1
Accelerating Quadratic Optimization with Reinforcement LearningCode1
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement LearningCode1
Actor-Attention-Critic for Multi-Agent Reinforcement LearningCode1
Deep Reinforcement Learning for Active High Frequency TradingCode1
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Benchmark Results

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