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 801–825 of 15113 papers

TitleStatusHype
Constrained Policy Optimization via Bayesian World ModelsCode1
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement LearningCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing ProblemsCode1
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter EfficientCode1
Dream and Search to Control: Latent Space Planning for Continuous ControlCode1
Active Inference for Stochastic ControlCode1
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Constrained Update Projection Approach to Safe Policy OptimizationCode1
DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learningCode1
Contextualize Me -- The Case for Context in Reinforcement LearningCode1
Dropout Q-Functions for Doubly Efficient Reinforcement LearningCode1
DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment RegimeCode1
DUMP: Automated Distribution-Level Curriculum Learning for RL-based LLM Post-trainingCode1
DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous ControlCode1
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RLCode1
Continuous-Time Model-Based Reinforcement LearningCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Edge Rewiring Goes Neural: Boosting Network Resilience without Rich FeaturesCode1
Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision TraceabilityCode1
Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement LearningCode1
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted PrescriptionCode1
Conservative Offline Distributional Reinforcement LearningCode1
Efficient Diffusion Policies for Offline Reinforcement LearningCode1
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement LearningCode1
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Benchmark Results

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