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 5151–5175 of 15113 papers

TitleStatusHype
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation—0
Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments—0
Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors—0
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning—0
Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning—0
Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning—0
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning—0
Human-like Energy Management Based on Deep Reinforcement Learning and Historical Driving Experiences—0
Human-Object Interaction from Human-Level Instructions—0
Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning—0
Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives—0
Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learning—0
Human-Timescale Adaptation in an Open-Ended Task Space—0
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
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Benchmark Results

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