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 8401–8425 of 15113 papers

TitleStatusHype
Online Data Poisoning Attack—0
Online Data Poisoning Attacks—0
Online Decision Based Visual Tracking via Reinforcement Learning—0
Online Decision MetaMorphFormer: A Casual Transformer-Based Reinforcement Learning Framework of Universal Embodied Intelligence—0
Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor Environments—0
Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback—0
Online Iterative Self-Alignment for Radiology Report Generation—0
Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar—0
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing—0
Online Learning for Stochastic Shortest Path Model via Posterior Sampling—0
Online Linear Regression and Its Application to Model-Based Reinforcement Learning—0
Online Meta-learning by Parallel Algorithm Competition—0
Online Model-Free Reinforcement Learning for the Automatic Control of a Flexible Wing Aircraft—0
Online Model Selection for Reinforcement Learning with Function Approximation—0
Online Monotone Games—0
Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control—0
Online Multimodal Transportation Planning using Deep Reinforcement Learning—0
Online Observer-Based Inverse Reinforcement Learning—0
Online Optimization of Curriculum Learning Schedules using Evolutionary Optimization—0
Online Phase Estimation of Human Oscillatory Motions using Deep Learning—0
Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams—0
Online Policies for Real-Time Control Using MRAC-RL—0
Online Policy Optimization for Robust MDP—0
Online Regret Bounds for Undiscounted Continuous Reinforcement Learning—0
Online Reinforcement Learning Control by Direct Heuristic Dynamic Programming: from Time-Driven to Event-Driven—0
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Benchmark Results

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