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

Showing 13811390 of 15113 papers

TitleStatusHype
End-to-End Urban Driving by Imitating a Reinforcement Learning CoachCode1
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement LearningCode1
Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessCode1
Aspect Sentiment Triplet Extraction Using Reinforcement LearningCode1
Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action SpacesCode1
Paint Transformer: Feed Forward Neural Painting with Stroke PredictionCode1
VeRLPy: Python Library for Verification of Digital Designs with Reinforcement LearningCode1
Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI EconomistCode1
The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement LearningCode1
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement LearningCode1
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Benchmark Results

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