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 2811–2820 of 15113 papers

TitleStatusHype
AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning—0
AutoDOViz: Human-Centered Automation for Decision Optimization—0
A Learned Simulation Environment to Model Student Engagement and Retention in Automated Online Courses—0
Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure—0
AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning—0
A Learned Simulation Environment to Model Plant Growth in Indoor Farming—0
Adaptive Discounting of Training Time Attacks—0
Auto-COP: Adaptation Generation in Context-Oriented Programming using Reinforcement Learning Options—0
Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning—0
Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search—0
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Benchmark Results

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