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 2791–2800 of 15113 papers

TitleStatusHype
Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device—0
Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application to Reinforcement Learning—0
Auto Graph Encoder-Decoder for Neural Network Pruning—0
Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning—0
DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning—0
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning—0
A Learning Framework for High Precision Industrial Assembly—0
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning—0
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization—0
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation—0
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Benchmark Results

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