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 561–570 of 15113 papers

TitleStatusHype
D2RL: Deep Dense Architectures in Reinforcement LearningCode1
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN PredictionCode1
A SWAT-based Reinforcement Learning Framework for Crop ManagementCode1
Curriculum-based Reinforcement Learning for Distribution System Critical Load RestorationCode1
Dataset Reset Policy Optimization for RLHFCode1
Deceptive Path Planning via Reinforcement Learning with Graph Neural NetworksCode1
Curious Hierarchical Actor-Critic Reinforcement LearningCode1
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
Aspect Sentiment Triplet Extraction Using Reinforcement LearningCode1
Asset Allocation: From Markowitz to Deep Reinforcement LearningCode1
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Benchmark Results

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