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 8676–8700 of 15113 papers

TitleStatusHype
Optimizing Medical Treatment for Sepsis in Intensive Care: from Reinforcement Learning to Pre-Trial Evaluation—0
Optimizing Memory Mapping Using Deep Reinforcement Learning—0
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning—0
Optimizing Multiagent Cooperation via Policy Evolution and Shared Experiences—0
Optimizing Navigation And Chemical Application in Precision Agriculture With Deep Reinforcement Learning And Conditional Action Tree—0
Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations—0
Optimizing Novelty of Top-k Recommendations using Large Language Models and Reinforcement Learning—0
Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework—0
Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities—0
Optimizing Quantum Error Correction Codes with Reinforcement Learning—0
Optimizing Query Evaluations using Reinforcement Learning for Web Search—0
Optimizing Routerless Network-on-Chip Designs: An Innovative Learning-Based Framework—0
Optimizing Sensor Redundancy in Sequential Decision-Making Problems—0
Optimizing Sponsored Search Ranking Strategy by Deep Reinforcement Learning—0
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining—0
Optimizing Tensor Network Contraction Using Reinforcement Learning—0
Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning—0
Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports—0
Optimizing the Long-Term Average Reward for Continuing MDPs: A Technical Report—0
Optimizing the Long-Term Behaviour of Deep Reinforcement Learning for Pushing and Grasping—0
Optimizing Traffic Lights with Multi-agent Deep Reinforcement Learning and V2X communication—0
Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning—0
Optimizing Wireless Discontinuous Reception via MAC Signaling Learning—0
Option Compatible Reward Inverse Reinforcement Learning—0
Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering—0
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Benchmark Results

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