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 5451–5475 of 15113 papers

TitleStatusHype
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks—0
Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing—0
Integrating Multi-Modal Input Token Mixer Into Mamba-Based Decision Models: Decision MetaMamba—0
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents—0
Integrating Pretrained Language Model for Dialogue Policy Learning—0
Integrating process design and control using reinforcement learning—0
Integrating Question Rewrites in Conversational Question Answering: A Reinforcement Learning Approach—0
Integrating Reinforcement Learning and AI Agents for Adaptive Robotic Interaction and Assistance in Dementia Care—0
Integrating Reinforcement Learning and Large Language Models for Crop Production Process Management Optimization and Control through A New Knowledge-Based Deep Learning Paradigm—0
Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays—0
Integrating Task-Motion Planning with Reinforcement Learning for Robust Decision Making in Mobile Robots—0
Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Graphical Model—0
INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning—0
Intelligence and Unambitiousness Using Algorithmic Information Theory—0
Intelligent Agricultural Management Considering N_2O Emission and Climate Variability with Uncertainties—0
Intelligent Autonomous Intersection Management—0
Intelligent Bandwidth Allocation for Latency Management in NG-EPON using Reinforcement Learning Methods—0
Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning—0
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning—0
Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning—0
Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learning—0
IntelligentPooling: Practical Thompson Sampling for mHealth—0
Intelligent problem-solving as integrated hierarchical reinforcement learning—0
Reconfigurable Intelligent Surface-assisted Multi-UAV Networks: Efficient Resource Allocation with Deep Reinforcement Learning—0
Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning—0
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Benchmark Results

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