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 2371–2380 of 15113 papers

TitleStatusHype
Flexible Robust Beamforming for Multibeam Satellite Downlink using Reinforcement LearningCode1
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement LearningCode3
Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test—0
Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials—0
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory—0
QF-tuner: Breaking Tradition in Reinforcement Learning—0
Feedback Efficient Online Fine-Tuning of Diffusion ModelsCode2
GenNBV: Generalizable Next-Best-View Policy for Active 3D ReconstructionCode2
How Can LLM Guide RL? A Value-Based ApproachCode1
Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning—0
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Benchmark Results

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