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 6226–6250 of 15113 papers

TitleStatusHype
Leveraging human Domain Knowledge to model an empirical Reward function for a Reinforcement Learning problem—0
Leveraging human knowledge in tabular reinforcement learning: A study of human subjects—0
Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains—0
Leveraging Modality-specific Representations for Audio-visual Speech Recognition via Reinforcement Learning—0
Leveraging Offline Data in Online Reinforcement Learning—0
Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments—0
Leveraging Partial SMILES Validation Scheme for Enhanced Drug Design in Reinforcement Learning Frameworks—0
PerfRL: A Small Language Model Framework for Efficient Code Optimization—0
Leveraging Reinforcement Learning for evaluating Robustness of KNN Search Algorithms—0
Leveraging Reinforcement Learning in Red Teaming for Advanced Ransomware Attack Simulations—0
Leveraging Reinforcement Learning Techniques for Effective Policy Adoption and Validation—0
Leveraging Reward Gradients For Reinforcement Learning in Differentiable Physics Simulations—0
Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning—0
Leveraging the Variance of Return Sequences for Exploration Policy—0
Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation—0
LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents—0
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems—0
Lifelong Learning for Fog Load Balancing: A Transfer Learning Approach—0
Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines—0
Lifelong Robotic Reinforcement Learning by Retaining Experiences—0
Lifted Model Checking for Relational MDPs—0
Lifting the veil on hyper-parameters for value-based deep reinforcement learning—0
Policy Gradient Methods for Distortion Risk Measures—0
lilGym: Natural Language Visual Reasoning with Reinforcement Learning—0
LiMIIRL: Lightweight Multiple-Intent Inverse Reinforcement Learning—0
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Benchmark Results

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