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 3141–3150 of 15113 papers

TitleStatusHype
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control—0
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning—0
Curiosity Based Reinforcement Learning on Robot Manufacturing Cell—0
Deep Reinforcement Learning Based Power Allocation for D2D Network—0
Curiosity-Driven Experience Prioritization via Density Estimation—0
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning—0
Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning—0
A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective—0
Curiosity-Driven Recommendation Strategy for Adaptive Learning via Deep Reinforcement Learning—0
CROPS: A Deployable Crop Management System Over All Possible State Availabilities—0
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Benchmark Results

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