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 3701–3725 of 15113 papers

TitleStatusHype
Costate-focused models for reinforcement learning—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
Deep Decentralized Reinforcement Learning for Cooperative Control—0
Deep reinforcement learning for RAN optimization and control—0
DIP-R1: Deep Inspection and Perception with RL Looking Through and Understanding Complex Scenes—0
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning—0
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments—0
Deep Reinforcement Learning for Resource Management in Network Slicing—0
Accelerating Stochastic Composition Optimization—0
Deep Reinforcement Learning for RIS-Assisted FD Systems: Single or Distributed RIS?—0
Corruption-Robust Offline Reinforcement Learning—0
Deep Reinforcement Learning for Robotic Manipulation-The state of the art—0
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment—0
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes—0
Deep Reinforcement Learning for Routing a Heterogeneous Fleet of Vehicles—0
Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers—0
Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection—0
Using Deep Reinforcement Learning for mmWave Real-Time Scheduling—0
Deep reinforcement learning for scheduling in large-scale networked control systems—0
Deep Reinforcement Learning for Scheduling in Cellular Networks—0
Deep reinforcement learning for search, recommendation, and online advertising: a survey—0
Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review—0
Corruption-robust exploration in episodic reinforcement learning—0
Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management—0
A stabilizing reinforcement learning approach for sampled systems with partially unknown models—0
Show:102550
← PrevPage 149 of 605Next →

Benchmark Results

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