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 341–350 of 15113 papers

TitleStatusHype
A Review of Safe Reinforcement Learning: Methods, Theory and ApplicationsCode2
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph MatchingCode2
Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language ModelsCode2
Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter ControlCode2
Learning to Predict Without Looking Ahead: World Models Without Forward PredictionCode2
ARPO:End-to-End Policy Optimization for GUI Agents with Experience ReplayCode2
Deep Reinforcement Learning for Multi-Agent InteractionCode2
DayDreamer: World Models for Physical Robot LearningCode2
D4RL: Datasets for Deep Data-Driven Reinforcement LearningCode2
Decoupling Representation Learning from Reinforcement LearningCode2
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Benchmark Results

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