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 1131–1140 of 15113 papers

TitleStatusHype
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
AutoPhoto: Aesthetic Photo Capture using Reinforcement LearningCode1
Efficient Pressure: Improving efficiency for signalized intersectionsCode1
Efficient Meta Reinforcement Learning for Preference-based Fast AdaptationCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior RegularizationCode1
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and PlanningCode1
Automating DBSCAN via Deep Reinforcement LearningCode1
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code SynthesisCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
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Benchmark Results

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