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 7026–7050 of 15113 papers

TitleStatusHype
Learning to Transfer Role Assignment Across Team Sizes—0
Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning—0
Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach—0
Efficient Reinforcement Learning for Unsupervised Controlled Text Generation—0
Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning—0
CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection—0
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular DatasetsCode0
The Importance of Credo in Multiagent Learning—0
Safe Reinforcement Learning Using Black-Box Reachability AnalysisCode0
Understanding Game-Playing Agents with Natural Language AnnotationsCode0
Methodical Advice Collection and Reuse in Deep Reinforcement Learning—0
Reinforcement Learning Policy Recommendation for Interbank Network Stability—0
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning—0
Flexible Multiple-Objective Reinforcement Learning for Chip Placement—0
Local Feature Swapping for Generalization in Reinforcement Learning—0
Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents—0
Self-critical Sequence Training for Automatic Speech Recognition—0
Modularity benefits reinforcement learning agents with competing homeostatic drives—0
RL-CoSeg : A Novel Image Co-Segmentation Algorithm with Deep Reinforcement Learning—0
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?—0
Forgetting and Imbalance in Robot Lifelong Learning with Off-policy Data—0
Smart Interference Management xApp using Deep Reinforcement Learning—0
A Reinforcement Learning Approach for Electric Vehicle Routing Problem with Vehicle-to-Grid Supply—0
AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection—0
An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning—0
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Benchmark Results

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