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 2091–2100 of 15113 papers

TitleStatusHype
Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail PromotionsCode0
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning—0
Stochastic Q-learning for Large Discrete Action Spaces—0
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—0
Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning—0
CarDreamer: Open-Source Learning Platform for World Model based Autonomous DrivingCode3
vMFER: Von Mises-Fisher Experience Resampling Based on Uncertainty of Gradient Directions for Policy Improvement—0
Reinformer: Max-Return Sequence Modeling for Offline RLCode1
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments—0
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Benchmark Results

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