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

Showing 12411250 of 15113 papers

TitleStatusHype
CertRL: Formalizing Convergence Proofs for Value and Policy Iteration in CoqCode1
CFR-RL: Traffic Engineering with Reinforcement Learning in SDNCode1
Action Space Shaping in Deep Reinforcement LearningCode1
Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward EnvironmentsCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
Generalization in Reinforcement Learning by Soft Data AugmentationCode1
Visual Grounding for Object-Level Generalization in Reinforcement LearningCode1
Energy Pricing in P2P Energy Systems Using Reinforcement LearningCode1
Generalizing Across Multi-Objective Reward Functions in Deep Reinforcement LearningCode1
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their SolutionsCode1
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Benchmark Results

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