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 1381–1390 of 15113 papers

TitleStatusHype
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster trainingCode1
From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement LearningCode1
Active Reinforcement Learning for Robust Building ControlCode1
Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning FrameworkCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-Per-SecondCode1
Gamma and Vega Hedging Using Deep Distributional Reinforcement LearningCode1
Aerial View Localization with Reinforcement Learning: Towards Emulating Search-and-RescueCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
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Benchmark Results

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