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 1081–1090 of 15113 papers

TitleStatusHype
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy GamesCode1
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted PrescriptionCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision TraceabilityCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
Effective Diversity in Population Based Reinforcement LearningCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
AutoPhoto: Aesthetic Photo Capture using Reinforcement LearningCode1
Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement LearningCode1
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Benchmark Results

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