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 3676–3700 of 15113 papers

TitleStatusHype
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems—0
Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations—0
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer—0
Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning—0
Deep reinforcement learning for optical systems: A case study of mode-locked lasers—0
Deep Reinforcement Learning for Optimal Control of Space Heating—0
Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks—0
A State Augmentation based approach to Reinforcement Learning from Human Preferences—0
Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning—0
Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information—0
Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology—0
Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems—0
Deep Reinforcement Learning for Option Replication and Hedging—0
Deep Reinforcement Learning for Organ Localization in CT—0
Deep Reinforcement Learning for Orienteering Problems Based on Decomposition—0
Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning—0
Deep Reinforcement Learning for Personalized Search Story Recommendation—0
Autonomous Quadrotor Landing using Deep Reinforcement Learning—0
Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming—0
Costate-focused models for reinforcement learning—0
Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems—0
Deep Decentralized Reinforcement Learning for Cooperative Control—0
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning—0
Deep Reinforcement Learning for Process Control: A Primer for Beginners—0
Accelerating Stochastic Composition Optimization—0
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Benchmark Results

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