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 3626–3650 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Automating the resolution of flight conflicts: Deep reinforcement learning in service of air traffic controllers—0
Deep Reinforcement Learning for Field Development Optimization—0
Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder—0
Deep Reinforcement Learning for Foreign Exchange Trading—0
Domain-adapted Learning and Interpretability: DRL for Gas Trading—0
Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing—0
Deep Reinforcement Learning for Green Security Games with Real-Time Information—0
Deep reinforcement learning for guidewire navigation in coronary artery phantom—0
Deep Reinforcement Learning for Haptic Shared Control in Unknown Tasks—0
Deep Reinforcement Learning for Heat Pump Control—0
Deep Reinforcement Learning for High Precision Assembly Tasks—0
Deep Reinforcement Learning for High Level Character Control—0
Deep Reinforcement Learning for Image Hashing—0
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning—0
Autonomous Attack Mitigation for Industrial Control Systems—0
Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces—0
Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings—0
Deep Reinforcement Learning for Intelligent Transportation Systems—0
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey—0
Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications—0
Costate-focused models for reinforcement learning—0
Deep Reinforcement Learning for Inverse Inorganic Materials Design—0
Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments—0
Deep Decentralized Reinforcement Learning for Cooperative Control—0
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Benchmark Results

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