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 1151–1175 of 15113 papers

TitleStatusHype
Asynchronous Reinforcement Learning for Real-Time Control of Physical RobotsCode1
Possibility Before Utility: Learning And Using Hierarchical AffordancesCode1
Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit DesignCode1
Reinforcement learning for automatic quadrilateral mesh generation: a soft actor-critic approachCode1
Teachable Reinforcement Learning via Advice DistillationCode1
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information CollaborationCode1
Latent-Variable Advantage-Weighted Policy Optimization for Offline RLCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Zipfian environments for Reinforcement LearningCode1
The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning AlgorithmsCode1
Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for applications in single-asset tradingCode1
Curriculum-based Reinforcement Learning for Distribution System Critical Load RestorationCode1
Deep Reinforcement Learning for Entity AlignmentCode1
Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer EmpowermentCode1
Influencing Long-Term Behavior in Multiagent Reinforcement LearningCode1
Testing Stationarity and Change Point Detection in Reinforcement LearningCode1
Affordance Learning from Play for Sample-Efficient Policy LearningCode1
Avalanche RL: a Continual Reinforcement Learning LibraryCode1
Combining Modular Skills in Multitask LearningCode1
Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network EmbeddingCode1
Building a 3-Player Mahjong AI using Deep Reinforcement LearningCode1
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RLCode1
Blockchain Framework for Artificial Intelligence ComputationCode1
Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in IntralogisticsCode1
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement LearningCode1
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Benchmark Results

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