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

Showing 11511200 of 15113 papers

TitleStatusHype
Possibility Before Utility: Learning And Using Hierarchical AffordancesCode1
Asynchronous Reinforcement Learning for Real-Time Control of Physical RobotsCode1
Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit DesignCode1
Teachable Reinforcement Learning via Advice DistillationCode1
Reinforcement learning for automatic quadrilateral mesh generation: a soft actor-critic approachCode1
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information CollaborationCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Latent-Variable Advantage-Weighted Policy Optimization for Offline RLCode1
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
Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer EmpowermentCode1
Deep Reinforcement Learning for Entity AlignmentCode1
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
Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network EmbeddingCode1
Avalanche RL: a Continual Reinforcement Learning LibraryCode1
Combining Modular Skills in Multitask LearningCode1
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
A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric VehiclesCode1
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement LearningCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban DrivingCode1
Open-Ended Reinforcement Learning with Neural Reward FunctionsCode1
Soft Actor-Critic Deep Reinforcement Learning for Fault Tolerant Flight ControlCode1
Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-DemandCode1
Safe Reinforcement Learning by Imagining the Near FutureCode1
QuadSim: A Quadcopter Rotational Dynamics Simulation Framework For Reinforcement Learning AlgorithmsCode1
Supported Policy Optimization for Offline Reinforcement LearningCode1
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement LearningCode1
Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning SystemsCode1
The Shapley Value in Machine LearningCode1
Reinforcement Learning with Sparse Rewards using Guidance from Offline DemonstrationCode1
Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RLCode1
Contextualize Me -- The Case for Context in Reinforcement LearningCode1
Approximating Gradients for Differentiable Quality Diversity in Reinforcement LearningCode1
Bingham Policy Parameterization for 3D Rotations in Reinforcement LearningCode1
Geometric Multimodal Contrastive Representation LearningCode1
Learning Synthetic Environments and Reward Networks for Reinforcement LearningCode1
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill DiversityCode1
Transfer Reinforcement Learning for Differing Action Spaces via Q-Network RepresentationsCode1
Adversarially Trained Actor Critic for Offline Reinforcement LearningCode1
Learning Interpretable, High-Performing Policies for Autonomous DrivingCode1
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy MatchingCode1
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Benchmark Results

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