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 5476–5500 of 15113 papers

TitleStatusHype
Intelligent Replication Management for HDFS Using Reinforcement Learning—0
Intelligent Residential Energy Management System using Deep Reinforcement Learning—0
Intelligent Resource Allocation in Dense LoRa Networks using Deep Reinforcement Learning—0
Intelligent Roundabout Insertion using Deep Reinforcement Learning—0
Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach—0
Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control—0
Intelligent Trajectory Planning in UAV-mounted Wireless Networks: A Quantum-Inspired Reinforcement Learning Perspective—0
Intelligent User Association for Symbiotic Radio Networks using Deep Reinforcement Learning—0
IntelliLung: Advancing Safe Mechanical Ventilation using Offline RL with Hybrid Actions and Clinically Aligned Rewards—0
Intent-aware Multi-agent Reinforcement Learning—0
Variational Policy Propagation for Multi-agent Reinforcement Learning—0
Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals—0
Interaction-Aware Parameter Privacy-Preserving Data Sharing in Coupled Systems via Particle Filter Reinforcement Learning—0
Interaction-limited Inverse Reinforcement Learning—0
Interactions between Representation Learning and Supervision—0
Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations—0
Interactive Double Deep Q-network: Integrating Human Interventions and Evaluative Predictions in Reinforcement Learning of Autonomous Driving—0
Interactive Inverse Reinforcement Learning for Cooperative Games—0
Interactive Learning from Natural Language and Demonstrations using Signal Temporal Logic—0
Interactive Lungs Auscultation with Reinforcement Learning Agent—0
Interactively shaping robot behaviour with unlabeled human instructions—0
Interactive OT Gym: A Reinforcement Learning-Based Interactive Optical tweezer (OT)-Driven Microrobotics Simulation Platform—0
Interactive Parallel Exploration for Reinforcement Learning in Continuous Action Spaces—0
Interactive Question Clarification in Dialogue via Reinforcement Learning—0
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning—0
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Benchmark Results

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