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 3791–3800 of 15113 papers

TitleStatusHype
To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs—0
Constrained Decision Transformer for Offline Safe Reinforcement LearningCode2
Regret-Based Defense in Adversarial Reinforcement LearningCode0
Semiconductor Fab Scheduling with Self-Supervised and Reinforcement LearningCode1
On Modeling Long-Term User Engagement from Stochastic Feedback—0
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement LearningCode1
A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning—0
Guiding Pretraining in Reinforcement Learning with Large Language ModelsCode1
Universal Agent Mixtures and the Geometry of Intelligence—0
Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with DistractionsCode0
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Benchmark Results

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