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

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Showing 3651–3675 of 15113 papers

TitleStatusHype
Agent-Agnostic Human-in-the-Loop Reinforcement Learning—0
Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment—0
Differentially Private Exploration in Reinforcement Learning with Linear Representation—0
A State Augmentation based approach to Reinforcement Learning from Human Preferences—0
Deep Reinforcement Learning for Long Term Hydropower Production Scheduling—0
Deep Reinforcement Learning for Long-Term Voltage Stability Control—0
Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality—0
Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning—0
Differentiable Discrete Event Simulation for Queuing Network Control—0
Deep Reinforcement Learning for mmWave Initial Beam Alignment—0
Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes—0
Deep Reinforcement Learning for Motion Planning of Mobile Robots—0
Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling—0
Deep Reinforcement Learning for Multi-objective Optimization—0
Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications—0
Costate-focused models for reinforcement learning—0
Deep Decentralized Reinforcement Learning for Cooperative Control—0
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants—0
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem—0
Deep Reinforcement Learning for Multi-Truck Vehicle Routing Problems with Multi-Leg Demand Routes—0
Deep Reinforcement Learning for Multi-user Massive MIMO with Channel Aging—0
Deep Reinforcement Learning for Navigation in AAA Video Games—0
Deep Reinforcement Learning for Neural Control—0
Deep Reinforcement Learning for NLP—0
Differentiable Logic Machines—0
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Benchmark Results

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