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 2671–2680 of 15113 papers

TitleStatusHype
Learning to Rewrite Prompts for Personalized Text Generation—0
Automatic Poetry Generation with Mutual Reinforcement Learning—0
Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning—0
ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning—0
Automatic, Personalized, and Flexible Playlist Generation using Reinforcement Learning—0
A Local Temporal Difference Code for Distributional Reinforcement Learning—0
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar—0
Automatic low-bit hybrid quantization of neural networks through meta learning—0
Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition—0
Adaptive Insurance Reserving with CVaR-Constrained Reinforcement Learning under Macroeconomic Regimes—0
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Benchmark Results

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