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 3941–3950 of 15113 papers

TitleStatusHype
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple ConstraintsCode1
A Deep Neural Network Algorithm for Linear-Quadratic Portfolio Optimization with MGARCH and Small Transaction Costs—0
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement LearningCode1
Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement LearningCode1
A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systems—0
SMART: Self-supervised Multi-task pretrAining with contRol Transformers—0
Explainable Deep Reinforcement Learning: State of the Art and Challenges—0
Autonomous particles—0
AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning—0
ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents—0
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Benchmark Results

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