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 4241–4250 of 15113 papers

TitleStatusHype
A Database of Multimodal Data to Construct a Simulated Dialogue Partner with Varying Degrees of Cognitive Health—0
A data-driven choice of misfit function for FWI using reinforcement learning—0
A Data-Driven Model-Reference Adaptive Control Approach Based on Reinforcement Learning—0
A Dataset for Developing and Benchmarking Active Vision—0
AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection—0
AdaWM: Adaptive World Model based Planning for Autonomous Driving—0
Adding Conditional Control to Diffusion Models with Reinforcement Learning—0
Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets—0
Addressing Extrapolation Error in Deep Offline Reinforcement Learning—0
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning—0
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Benchmark Results

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