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 1626–1650 of 15113 papers

TitleStatusHype
Decoupled Prioritized Resampling for Offline RLCode1
Offline Reinforcement Learning for Visual NavigationCode1
Offline Reinforcement Learning from Images with Latent Space ModelsCode1
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience ReplayCode1
DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learningCode1
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point CloudsCode1
Abstract-to-Executable Trajectory Translation for One-Shot Task GeneralizationCode1
DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment RegimeCode1
Offline Reinforcement Learning with Reverse Model-based ImaginationCode1
A Text-based Deep Reinforcement Learning Framework for Interactive RecommendationCode1
DUMP: Automated Distribution-Level Curriculum Learning for RL-based LLM Post-trainingCode1
DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom RepresentationsCode1
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value RegularizationCode1
GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic EnvironmentsCode1
Goal-Aware Cross-Entropy for Multi-Target Reinforcement LearningCode1
Bayesian Generational Population-Based TrainingCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Behavior Proximal Policy OptimizationCode1
Dynamic Sparse Training for Deep Reinforcement LearningCode1
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGymCode1
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN PredictionCode1
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate ProgressCode1
Edge Rewiring Goes Neural: Boosting Network Resilience without Rich FeaturesCode1
Goal-Conditioned Generators of Deep PoliciesCode1
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Benchmark Results

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