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 3981–3990 of 15113 papers

TitleStatusHype
Fairness in Reinforcement Learning: A Survey—0
Dominion: A New Frontier for AI Research—0
Improving Targeted Molecule Generation through Language Model Fine-Tuning Via Reinforcement Learning—0
Space Processor Computation Time Analysis for Reinforcement Learning and Run Time Assurance Control Policies—0
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models—0
Fast Stochastic Policy Gradient: Negative Momentum for Reinforcement Learning—0
Improving Offline Reinforcement Learning with Inaccurate Simulators—0
SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory SystemsCode0
Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach—0
Genetic Drift Regularization: on preventing Actor Injection from breaking Evolution Strategies—0
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Benchmark Results

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