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 11401–11425 of 15113 papers

TitleStatusHype
Periodic Intra-Ensemble Knowledge Distillation for Reinforcement LearningCode0
PolicyGNN: Aggregation Optimization for Graph Neural Networks—0
Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning—0
Preventing Imitation Learning with Adversarial Policy Ensembles—0
A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation—0
Locally Private Distributed Reinforcement Learning—0
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles—0
Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning—0
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning—0
Distal Explanations for Model-free Explainable Reinforcement Learning—0
Data-driven control of micro-climate in buildings: an event-triggered reinforcement learning approach—0
Real-time calibration of coherent-state receivers: learning by trial and errorCode0
Some Insights into Lifelong Reinforcement Learning SystemsCode0
Rotation, Translation, and Cropping for Zero-Shot GeneralizationCode0
Unsupervised Program Synthesis for Images By Sampling Without Replacement—0
Reinforcement Learning-based Application Autoscaling in the Cloud: A Survey—0
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement LearningCode0
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning—0
Computing the Feedback Capacity of Finite State Channels using Reinforcement LearningCode0
Constrained Upper Confidence Reinforcement Learning—0
Tractable Reinforcement Learning of Signal Temporal Logic ObjectivesCode0
Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning—0
Multitask radiological modality invariant landmark localization using deep reinforcement learningCode0
Following Instructions by Imagining and Reaching Visual Goals—0
Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment—0
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Benchmark Results

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