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 11451–11475 of 15113 papers

TitleStatusHype
Machine versus Human Attention in Deep Reinforcement Learning Tasks—0
Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidance—0
HVAC-DPT: A Decision Pretrained Transformer for HVAC Control—0
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation—0
Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving—0
Hybrid Adversarial Imitation Learning—0
Hybrid Beamforming for mmWave MU-MISO Systems Exploiting Multi-agent Deep Reinforcement Learning—0
Hybrid computer approach to train a machine learning system—0
Hybrid Cross-domain Robust Reinforcement Learning—0
Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving—0
Hybrid Imitation Learning for Real-Time Service Restoration in Resilient Distribution Systems—0
Hybrid Indoor Localization via Reinforcement Learning-based Information Fusion—0
Hybrid Information-driven Multi-agent Reinforcement Learning—0
Hybridization of evolutionary algorithm and deep reinforcement learning for multi-objective orienteering optimization—0
Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks—0
Hybrid Learning with New Value Function for the Maximum Common Subgraph Problem—0
Hybrid Policies Using Inverse Rewards for Reinforcement Learning—0
Hybrid Q-Learning Applied to Ubiquitous recommender system—0
Hybrid Reinforcement Learning and Model Predictive Control for Adaptive Control of Hydrogen-Diesel Dual-Fuel Combustion—0
Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections—0
Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs—0
Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks—0
Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer—0
Hybrid Reinforcement Learning Framework for Mixed-Variable Problems—0
Hybrid Reinforcement Learning from Offline Observation Alone—0
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Benchmark Results

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