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 1561–1570 of 15113 papers

TitleStatusHype
Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm—0
Stage-Wise Reward Shaping for Acrobatic Robots: A Constrained Multi-Objective Reinforcement Learning ApproachCode2
Energy Saving in 6G O-RAN Using DQN-based xApp—0
Physics Enhanced Residual Policy Learning (PERPL) for safety cruising in mixed traffic platooning under actuator and communication delay—0
Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach—0
A novel agent with formal goal-reaching guarantees: an experimental study with a mobile robot—0
CANDERE-COACH: Reinforcement Learning from Noisy Feedback—0
A Distribution-Aware Flow-Matching for Generating Unstructured Data for Few-Shot Reinforcement Learning—0
OMG-RL:Offline Model-based Guided Reward Learning for Heparin Treatment—0
Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling—0
Show:102550
← PrevPage 157 of 1512Next →

Benchmark Results

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