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 6901–6925 of 15113 papers

TitleStatusHype
Turning Mathematics Problems into Games: Reinforcement Learning and Gröbner bases together solve Integer Feasibility Problems—0
Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning—0
Tutorial on Course-of-Action (COA) Attack Search Methods in Computer Networks—0
Tutoring Reinforcement Learning via Feedback Control—0
TW-CRL: Time-Weighted Contrastive Reward Learning for Efficient Inverse Reinforcement Learning—0
Twisting Lids Off with Two Hands—0
Two Approaches to Building Collaborative, Task-Oriented Dialog Agents through Self-Play—0
Two Can Play That Game: An Adversarial Evaluation of a Cyber-alert Inspection System—0
Two-dimensional Anti-jamming Mobile Communication Based on Reinforcement Learning—0
Two geometric input transformation methods for fast online reinforcement learning with neural nets—0
Two-Hop Age of Information Scheduling for Multi-UAV Assisted Mobile Edge Computing: FRL vs MADDPG—0
Two-stage Deep Reinforcement Learning for Inverter-based Volt-VAR Control in Active Distribution Networks—0
Efficiently Training Deep-Learning Parametric Policies using Lagrangian Duality—0
Two-stage training algorithm for AI robot soccer—0
Two-Step Reinforcement Learning for Multistage Strategy Card Game—0
UAS Navigation in the Real World Using Visual Observation—0
UAS Visual Navigation in Large and Unseen Environments via a Meta Agent—0
UAV aided Metaverse over Wireless Communications: A Reinforcement Learning Approach—0
UAV Aided Search and Rescue Operation Using Reinforcement Learning—0
UAV-Assisted Coverage Hole Detection Using Reinforcement Learning in Urban Cellular Networks—0
UAV-Assisted Enhanced Coverage and Capacity in Dynamic MU-mMIMO IoT Systems: A Deep Reinforcement Learning Approach—0
UAV-assisted Semantic Communication with Hybrid Action Reinforcement Learning—0
UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations—0
UAV Path Planning Employing MPC- Reinforcement Learning Method Considering Collision Avoidance—0
UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning—0
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Benchmark Results

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