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 10076–10100 of 15113 papers

TitleStatusHype
Optimizing AD Pruning of Sponsored Search with Reinforcement Learning—0
Robust Deep Reinforcement Learning through Adversarial LossCode1
Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement LearningCode1
Area-wide traffic signal control based on a deep graph Q-Network (DGQN) trained in an asynchronous manner—0
Aligning AI With Shared Human ValuesCode2
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles—0
Faded-Experience Trust Region Policy Optimization for Model-Free Power Allocation in Interference Channel—0
Explanation of Reinforcement Learning Model in Dynamic Multi-Agent System—0
A Relearning Approach to Reinforcement Learning for Control of Smart Buildings—0
EasyRL: A Simple and Extensible Reinforcement Learning Framework—0
Learning Transition Models with Time-delayed Causal Relations—0
Robust Reinforcement Learning using Adversarial PopulationsCode1
Reinforced Epidemic Control: Saving Both Lives and EconomyCode1
Fully Decentralized Reinforcement Learning-based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Power—0
Learning to Play Two-Player Perfect-Information Games without Knowledge—0
Learning Agile Locomotion via Adversarial Training—0
Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning—0
Cooperative Control of Mobile Robots with Stackelberg Learning—0
Proximal Deterministic Policy Gradient—0
Tracking the Race Between Deep Reinforcement Learning and Imitation Learning -- Extended Version—0
Curriculum Learning with a Progression Function—0
Spatial Geometric Reasoning for Room Layout Estimation via Deep Reinforcement Learning—0
Neural Batch Sampling with Reinforcement Learning for Semi-Supervised Anomaly Detection—0
Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs—0
Deep Reinforcement Learning Based Mobile Edge Computing for Intelligent Internet of Things—0
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Benchmark Results

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