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 476–500 of 15113 papers

TitleStatusHype
Exploiting Hybrid Policy in Reinforcement Learning for Interpretable Temporal Logic ManipulationCode1
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous InferenceCode1
RL-LLM-DT: An Automatic Decision Tree Generation Method Based on RL Evaluation and LLM EnhancementCode1
Entropy-Regularized Process Reward ModelCode1
Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement LearningCode1
Are Expressive Models Truly Necessary for Offline RL?Code1
Reinforcement Learning Policy as Macro Regulator Rather than Macro PlacerCode1
M^3PC: Test-time Model Predictive Control for Pretrained Masked Trajectory ModelCode1
Mind the Gap: Towards Generalizable Autonomous Penetration Testing via Domain Randomization and Meta-Reinforcement LearningCode1
AI-Driven Day-to-Day Route ChoiceCode1
Multi-Agent Environments for Vehicle Routing ProblemsCode1
LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog CircuitsCode1
Doubly Mild Generalization for Offline Reinforcement LearningCode1
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
Zonal RL-RRT: Integrated RL-RRT Path Planning with Collision Probability and Zone ConnectivityCode1
Reinforcement Learning Gradients as Vitamin for Online Finetuning Decision TransformersCode1
Online Intrinsic Rewards for Decision Making Agents from Large Language Model FeedbackCode1
Learning Successor Features the Simple WayCode1
A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics TasksCode1
Offline Reinforcement Learning with OOD State Correction and OOD Action SuppressionCode1
Leveraging Skills from Unlabeled Prior Data for Efficient Online ExplorationCode1
Reinforced Imitative Trajectory Planning for Urban Automated DrivingCode1
Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement LearningCode1
Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning AgentsCode1
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter EfficientCode1
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Benchmark Results

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