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 4061–4070 of 15113 papers

TitleStatusHype
SmartPathfinder: Pushing the Limits of Heuristic Solutions for Vehicle Routing Problem with Drones Using Reinforcement Learning—0
Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement LearningCode0
Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation—0
Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains—0
FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning—0
Efficient Duple Perturbation Robustness in Low-rank MDPs—0
Enhancing Policy Gradient with the Polyak Step-Size Adaption—0
On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning—0
UAV-Assisted Enhanced Coverage and Capacity in Dynamic MU-mMIMO IoT Systems: A Deep Reinforcement Learning Approach—0
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery—0
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Benchmark Results

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