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 1001–1025 of 15113 papers

TitleStatusHype
Aerial View Localization with Reinforcement Learning: Towards Emulating Search-and-RescueCode1
Hearts Gym: Learning Reinforcement Learning as a Team EventCode1
Actor Prioritized Experience ReplayCode1
Cell-Free Latent Go-ExploreCode1
Style-Agnostic Reinforcement LearningCode1
Rethinking Conversational Recommendations: Is Decision Tree All You Need?Code1
Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement LearningCode1
Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement LearningCode1
Light-weight probing of unsupervised representations for Reinforcement LearningCode1
Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous DrivingCode1
Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement LearningCode1
PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning AlgorithmCode1
Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraftCode1
Towards Sequence-Level Training for Visual TrackingCode1
A Modular Framework for Reinforcement Learning Optimal ExecutionCode1
Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement LearningCode1
Robust Reinforcement Learning using Offline DataCode1
Automating DBSCAN via Deep Reinforcement LearningCode1
Basis for Intentions: Efficient Inverse Reinforcement Learning using Past ExperienceCode1
From Scratch to Sketch: Deep Decoupled Hierarchical Reinforcement Learning for Robotic Sketching AgentCode1
Object Detection with Deep Reinforcement LearningCode1
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement LearningCode1
Model-based graph reinforcement learning for inductive traffic signal controlCode1
Performance Comparison of Deep RL Algorithms for Energy Systems Optimal SchedulingCode1
Relay Hindsight Experience Replay: Self-Guided Continual Reinforcement Learning for Sequential Object Manipulation Tasks with Sparse RewardsCode1
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Benchmark Results

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