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 1751–1775 of 15113 papers

TitleStatusHype
Hindsight Experience ReplayCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
An empirical investigation of the challenges of real-world reinforcement learningCode1
Execution-based Code Generation using Deep Reinforcement LearningCode1
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action ConstraintsCode1
Hindsight Preference Learning for Offline Preference-based Reinforcement LearningCode1
Hierarchical Reinforcement Learning with Timed SubgoalsCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Simplified Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine TranslationCode1
Explainable Reinforcement Learning for Longitudinal ControlCode1
Hierarchical Skills for Efficient ExplorationCode1
Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement LearningCode1
Explainable Reinforcement Learning via a Causal World ModelCode1
Explaining Autonomous Driving Actions with Visual Question AnsweringCode1
RealAnt: An Open-Source Low-Cost Quadruped for Education and Research in Real-World Reinforcement LearningCode1
Behavior Proximal Policy OptimizationCode1
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowCode1
Hierarchical Reinforcement Learning for Power Network Topology ControlCode1
Hierarchical Kickstarting for Skill Transfer in Reinforcement LearningCode1
Behavior From the Void: Unsupervised Active Pre-TrainingCode1
Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing ProblemsCode1
Automatic Data Augmentation for Generalization in Deep Reinforcement LearningCode1
Automatic Data Augmentation for Generalization in Reinforcement LearningCode1
Hierarchical clustering in particle physics through reinforcement learningCode1
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Benchmark Results

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