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

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Showing 10601–10625 of 15113 papers

TitleStatusHype
Challenging common bolus advisor for self-monitoring type-I diabetes patients using Reinforcement LearningCode0
Bridging the Imitation Gap by Adaptive Insubordination—0
Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems—0
Adaptive Traffic Control with Deep Reinforcement Learning:Towards State-of-the-art and BeyondCode0
Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning—0
On the Convergence of Reinforcement Learning with Monte Carlo Exploring Starts—0
Soft Expert Reward Learning for Vision-and-Language Navigation—0
Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense—0
Lagrangian Duality in Reinforcement Learning—0
A Short Note on Soft-max and Policy Gradients in Bandits Problems—0
A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks—0
Interpretable Control by Reinforcement Learning—0
An Overview of Natural Language State Representation for Reinforcement Learning—0
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search—0
Quick Question: Interrupting Users for Microtasks with Reinforcement Learning—0
Structure Mapping for Transferability of Causal ModelsCode0
Hyperparameter Selection for Offline Reinforcement Learning—0
Hierarchical Deep Reinforcement Learning Approach for Multi-Objective Scheduling With Varying Queue Sizes—0
Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study—0
CoNES: Convex Natural Evolutionary Strategies—0
Human-like Energy Management Based on Deep Reinforcement Learning and Historical Driving Experiences—0
Collision Avoidance Robotics Via Meta-Learning (CARML)Code0
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning—0
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators—0
DRIFT: Deep Reinforcement Learning for Functional Software Testing—0
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Benchmark Results

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