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 6926–6950 of 15113 papers

TitleStatusHype
Enabling A Network AI Gym for Autonomous Cyber Agents—0
Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges—0
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition—0
Enabling surrogate-assisted evolutionary reinforcement learning via policy embedding—0
Encoding priors in the brain: a reinforcement learning model for mouse decision making—0
End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learning—0
End-to-end Active Object Tracking via Reinforcement Learning—0
End-to-end Deep Reinforcement Learning Based Coreference Resolution—0
End-to-End Deep Reinforcement Learning for Lane Keeping Assist—0
End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning—0
End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning—0
End-to-end Lidar-Driven Reinforcement Learning for Autonomous Racing—0
End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning—0
End-to-End Motion Planning of Quadrotors Using Deep Reinforcement Learning—0
End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient—0
End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning—0
End-to-End Policy Gradient Method for POMDPs and Explainable Agents—0
End-to-End Race Driving with Deep Reinforcement Learning—0
End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation—0
End-to-End Vision-Based Adaptive Cruise Control (ACC) Using Deep Reinforcement Learning—0
Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space—0
Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach—0
Energy-aware Scheduling of Jobs in Heterogeneous Cluster Systems Using Deep Reinforcement Learning—0
Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks—0
Energy Efficiency Optimization for Subterranean LoRaWAN Using A Reinforcement Learning Approach: A Direct-to-Satellite Scenario—0
Show:102550
← PrevPage 278 of 605Next →

Benchmark Results

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