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 4601–4610 of 15113 papers

TitleStatusHype
Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability CertificateCode1
Adaptive patch foraging in deep reinforcement learning agents—0
Query Rewriting for Effective Misinformation Discovery—0
Abstract-to-Executable Trajectory Translation for One-Shot Task GeneralizationCode1
Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement LearningCode0
A Reinforcement Learning Approach to Estimating Long-term Treatment Effects—0
Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point CloudsCode1
A Scalable Finite Difference Method for Deep Reinforcement Learning—0
Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic LocomotionCode0
Deep reinforcement learning for automatic run-time adaptation of UWB PHY radio settings—0
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Benchmark Results

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