Reinforcement Learning

GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed to perform scalable reinforcement learning …
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A Deep Reinforcement Learning-driven Vine Copula Method for Correlation Structure Analysis of Mortgage
Controlling risk is the key to playing a core role in financial services and effectively serving the high-quality development of the …
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Learning to Shape Rewards using a Game of Two Partners
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards. …
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Quality-Similar Diversity via Population Based Reinforcement Learning
Diversity is a growing research topic in Reinforcement Learning (RL). Previous research on diversity has mainly focused on promoting …
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Solving Inventory Management Problems through Deep Reinforcement Learning
Inventory management (e.g. lost sales) is a central problem in supply chain management. Lost sales inventory systems with lead times …
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MSRL: Distributed Reinforcement Learning with Dataflow Fragments
Reinforcement learning (RL) trains many agents, which is resource-intensive and must scale to large GPU clusters. Different RL training …
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Meta-Reward-Net: Implicitly Differentiable Reward Learning for Preference-based Reinforcement Learning
Setting up a well-designed reward function has been challenging for many reinforcement learning applications. Preference-based …
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Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
Achieving human-level dexterity is an important open problem in robotics. However, tasks of dexterous hand manipulation even at the …
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End-to-End Affordance Learning for Robotic Manipulation
Learning to manipulate 3D objects in an interactive environment has been a challenging problem in Reinforcement Learning (RL). In …
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