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 926–950 of 15113 papers

TitleStatusHype
ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement LearningCode1
PaCo: Parameter-Compositional Multi-Task Reinforcement LearningCode1
Hypernetworks in Meta-Reinforcement LearningCode1
MoCoDA: Model-based Counterfactual Data AugmentationCode1
RMBench: Benchmarking Deep Reinforcement Learning for Robotic Manipulator ControlCode1
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement LearningCode1
Rethinking Value Function Learning for Generalization in Reinforcement LearningCode1
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain AdaptationCode1
Deep Black-Box Reinforcement Learning with Movement PrimitivesCode1
On Uncertainty in Deep State Space Models for Model-Based Reinforcement LearningCode1
Teacher Forcing Recovers Reward Functions for Text GenerationCode1
A Policy-Guided Imitation Approach for Offline Reinforcement LearningCode1
When to Update Your Model: Constrained Model-based Reinforcement LearningCode1
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization AlgorithmCode1
Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability CertificateCode1
Skill-Based Reinforcement Learning with Intrinsic Reward MatchingCode1
Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point CloudsCode1
WILD-SCAV: Benchmarking FPS Gaming AI on Unity3D-based EnvironmentsCode1
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement LearningCode1
Abstract-to-Executable Trajectory Translation for One-Shot Task GeneralizationCode1
A Mixture of Surprises for Unsupervised Reinforcement LearningCode1
Sustainable Online Reinforcement Learning for Auto-biddingCode1
Visual Reinforcement Learning with Self-Supervised 3D RepresentationsCode1
Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement LearningCode1
Multi-agent Dynamic Algorithm ConfigurationCode1
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Benchmark Results

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