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 3071–3080 of 15113 papers

TitleStatusHype
A Survey on Deep Reinforcement Learning for Data Processing and Analytics—0
A Survey on Deep Reinforcement Learning for Audio-Based Applications—0
Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning—0
Accidental exploration through value predictors—0
AcceRL: Policy Acceleration Framework for Deep Reinforcement Learning—0
A Survey on Deep Reinforcement Learning-based Approaches for Adaptation and Generalization—0
A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective—0
A Geometric Perspective on Visual Imitation Learning—0
A Survey on Causal Reinforcement Learning—0
A Survey of Text Games for Reinforcement Learning informed by Natural Language—0
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Benchmark Results

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