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 1041–1050 of 15113 papers

TitleStatusHype
Exploring the Technology Landscape through Topic Modeling, Expert Involvement, and Reinforcement Learning—0
On Generalization and Distributional Update for Mimicking Observations with Adequate Exploration—0
Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization—0
Evolution and The Knightian Blindspot of Machine Learning—0
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement LearningCode15
AdaWM: Adaptive World Model based Planning for Autonomous Driving—0
Reinforcement Learning Constrained Beam Search for Parameter Optimization of Paper Drying Under Flexible Constraints—0
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression—0
Extend Adversarial Policy Against Neural Machine Translation via Unknown Token—0
Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardwareCode0
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Benchmark Results

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