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 4691–4700 of 15113 papers

TitleStatusHype
Reinforcement Learning for Robust Header Compression under Model Uncertainty—0
Offline to Online Learning for Real-Time Bandwidth Estimation—0
Robotic Offline RL from Internet Videos via Value-Function Pre-Training—0
H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps—0
Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution—0
Delays in Reinforcement Learning—0
Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces—0
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward—0
Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rules—0
Privileged to Predicted: Towards Sensorimotor Reinforcement Learning for Urban Driving—0
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Benchmark Results

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