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 5076–5100 of 15113 papers

TitleStatusHype
Towards Augmented Microscopy with Reinforcement Learning-Enhanced WorkflowsCode0
Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents—0
Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies—0
Joint Sensing and Communications for Deep Reinforcement Learning-based Beam Management in 6G—0
Deep VULMAN: A Deep Reinforcement Learning-Enabled Cyber Vulnerability Management Framework—0
AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning—0
A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN—0
Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess—0
Chemotaxis of sea urchin sperm cells through deep reinforcement learning—0
Smart caching in a Data Lake for High Energy Physics analysis—0
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement LearningCode1
Deep Reinforcement Learning for Multi-Agent InteractionCode2
Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling—0
Learning to Grasp on the Moon from 3D Octree Observations with Deep Reinforcement Learning—0
Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot—0
A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning—0
Model-based graph reinforcement learning for inductive traffic signal controlCode1
Performance Comparison of Deep RL Algorithms for Energy Systems Optimal SchedulingCode1
Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction ApproachCode0
VacciNet: Towards a Smart Framework for Learning the Distribution Chain Optimization of Vaccines for a Pandemic—0
Retrieval of surgical phase transitions using reinforcement learning—0
Relay Hindsight Experience Replay: Self-Guided Continual Reinforcement Learning for Sequential Object Manipulation Tasks with Sparse RewardsCode1
Robot Policy Learning from Demonstration Using Advantage Weighting and Early Termination—0
Using Chatbots to Teach Languages—0
Learning to generate Reliable Broadcast Algorithms—0
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Benchmark Results

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