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 491–500 of 15113 papers

TitleStatusHype
ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial MarketsCode1
Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet DetectionCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement LearningCode1
Avalon: A Benchmark for RL Generalization Using Procedurally Generated WorldsCode1
Deep Reinforcement Learning for Active Human Pose EstimationCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement LearningCode1
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Benchmark Results

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