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 701–725 of 15113 papers

TitleStatusHype
Asynchronous Methods for Deep Reinforcement LearningCode1
Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional CurriculumCode1
Deep Reinforcement Learning For Sequence to Sequence ModelsCode1
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly DataCode1
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement LearningCode1
Affordance Learning from Play for Sample-Efficient Policy LearningCode1
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous ControlCode1
Automating DBSCAN via Deep Reinforcement LearningCode1
Actor-Attention-Critic for Multi-Agent Reinforcement LearningCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
De novo PROTAC design using graph-based deep generative modelsCode1
Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring RotorsCode1
Accelerating lifelong reinforcement learning via reshaping rewardsCode1
Adversarial Policies: Attacking Deep Reinforcement LearningCode1
Deep Reinforcement Learning for List-wise RecommendationsCode1
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot LearningCode1
Active MR k-space Sampling with Reinforcement LearningCode1
Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book ModelCode1
Deep Reinforcement Learning for Process SynthesisCode1
Deep Reinforcement Learning for Entity AlignmentCode1
Deep Reinforcement Learning for Cost-Effective Medical DiagnosisCode1
Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular NetworksCode1
Adversarially Trained Actor Critic for Offline Reinforcement LearningCode1
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Benchmark Results

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