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 1201–1225 of 15113 papers

TitleStatusHype
Faster Deep Reinforcement Learning with Slower Online NetworkCode1
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
Game-Theoretic Multiagent Reinforcement LearningCode1
Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet DetectionCode1
Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as ObservationCode1
Deep Reinforcement Learning Control of Quantum CartpolesCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
Deep Reinforcement Learning for Adaptive Exploration of Unknown EnvironmentsCode1
Deep Reinforcement Learning for Band Selection in Hyperspectral Image ClassificationCode1
Deep Reinforcement Learning for Computational Fluid Dynamics on HPC SystemsCode1
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human PlayerCode1
Deep Reinforcement Learning for Entity AlignmentCode1
A Distributional Perspective on Reinforcement LearningCode1
Deep reinforcement learning for large-scale epidemic controlCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
Deep Reinforcement Learning For Sequence to Sequence ModelsCode1
AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language ModelsCode1
Compile Scene Graphs with Reinforcement LearningCode1
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly DataCode1
Deep Reinforcement Learning from Self-Play in Imperfect-Information GamesCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
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Benchmark Results

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