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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1401–1425 of 15113 papers

TitleStatusHype
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Giraffe: Using Deep Reinforcement Learning to Play ChessCode1
Geometric Multimodal Contrastive Representation LearningCode1
GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic EnvironmentsCode1
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point CloudsCode1
Goal-Aware Cross-Entropy for Multi-Target Reinforcement LearningCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Graph Constrained Reinforcement Learning for Natural Language Action SpacesCode1
Graph Convolutional Memory using Topological PriorsCode1
Combining Modular Skills in Multitask LearningCode1
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand SystemsCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Affordance Learning from Play for Sample-Efficient Policy LearningCode1
CommonPower: A Framework for Safe Data-Driven Smart Grid ControlCode1
GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systemsCode1
Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement LearningCode1
Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for RoboticsCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Guiding Online Reinforcement Learning with Action-Free Offline PretrainingCode1
Guiding Pretraining in Reinforcement Learning with Large Language ModelsCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement LearningCode1
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Benchmark Results

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