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 3801–3810 of 15113 papers

TitleStatusHype
Learned Graph Rewriting with Equality Saturation: A New Paradigm in Relational Query Rewrite and Beyond—0
Optimizing Wireless Discontinuous Reception via MAC Signaling Learning—0
Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols—0
More Efficient Randomized Exploration for Reinforcement Learning via Approximate SamplingCode0
Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning—0
Physics-informed Imitative Reinforcement Learning for Real-world Driving—0
A Systematization of the Wagner Framework: Graph Theory Conjectures and Reinforcement LearningCode0
Quantum Compiling with Reinforcement Learning on a Superconducting Processor—0
Order-Optimal Instance-Dependent Bounds for Offline Reinforcement Learning with Preference Feedback—0
Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection—0
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Benchmark Results

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