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 8751–8775 of 15113 papers

TitleStatusHype
Learning to Minimize Age of Information over an Unreliable Channel with Energy Harvesting—0
Koopman Spectrum Nonlinear Regulators and Efficient Online LearningCode0
Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning—0
Understanding Adversarial Attacks on Observations in Deep Reinforcement LearningCode0
Reinforcement Learning based Disease Progression Model for Alzheimer's Disease—0
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL—0
Structure-aware reinforcement learning for node-overload protection in mobile edge computing—0
Deep Multiagent Reinforcement Learning: Challenges and Directions—0
Analysis and Control of a Planar QuadrotorCode0
DRILL-- Deep Reinforcement Learning for Refinement Operators in ALC—0
Convergent and Efficient Deep Q Network AlgorithmCode0
Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision ProcessesCode0
Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation—0
Action Set Based Policy Optimization for Safe Power Grid Management—0
Data-driven Model Predictive and Reinforcement Learning Based Control for Building Energy Management: a Survey—0
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples—0
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment—0
Regret Analysis in Deterministic Reinforcement Learning—0
A Reinforcement Learning Approach for Sequential Spatial Transformer Networks—0
Concentration of Contractive Stochastic Approximation and Reinforcement Learning—0
Continuous Control with Deep Reinforcement Learning for Autonomous Vessels—0
Discovering Generalizable Skills via Automated Generation of Diverse Tasks—0
Intrinsically Motivated Self-supervised Learning in Reinforcement Learning—0
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning—0
Branch Prediction as a Reinforcement Learning Problem: Why, How and Case Studies—0
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Benchmark Results

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