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 11226–11250 of 15113 papers

TitleStatusHype
Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning—0
Reinforcement Learning for Mitigating Intermittent Interference in Terahertz Communication Networks—0
Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces—0
Transfer Reinforcement Learning under Unobserved Contextual Information—0
Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison—0
Stable Policy Optimization via Off-Policy Divergence RegularizationCode0
Advancing Renewable Electricity Consumption With Reinforcement Learning—0
Human AI interaction loop training: New approach for interactive reinforcement learning—0
Deep Adversarial Reinforcement Learning for Object Disentangling—0
Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate—0
Reinforcement Learning Based Cooperative Coded Caching under Dynamic Popularities in Ultra-Dense Networks—0
Reinforcement Learning for Combinatorial Optimization: A Survey—0
Convergence of Q-value in case of Gaussian rewards—0
Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning—0
Lane-Merging Using Policy-based Reinforcement Learning and Post-Optimization—0
Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning—0
Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing—0
Distributional Robustness and Regularization in Reinforcement Learning—0
Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning—0
A Geometric Perspective on Visual Imitation Learning—0
Deep Reinforcement Learning-BasedRobust Protection in DER-Rich Distribution Grids—0
Dynamic Experience Replay—0
Efficient statistical validation with edge cases to evaluate Highly Automated Vehicles—0
Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation—0
Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning—0
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Benchmark Results

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