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 2701–2710 of 15113 papers

TitleStatusHype
Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities—0
Integrated Drill Boom Hole-Seeking Control via Reinforcement Learning—0
Self-Critical Alternate Learning based Semantic Broadcast Communication—0
Learning Curricula in Open-Ended Worlds—0
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning—0
DDxT: Deep Generative Transformer Models for Differential DiagnosisCode0
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning—0
Harnessing Discrete Representations For Continual Reinforcement LearningCode1
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning—0
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning ApproachCode1
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Benchmark Results

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