RIFLING: A reinforcement learning‐based GPU scheduler for deep learning research and development platforms

2021 ◽  
Author(s):  
Zhaoyun Chen
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Research And Development ◽  
Learning Research

scholarly journals RLCard: A Platform for Reinforcement Learning in Card Games

2020 ◽  
Author(s):  
Daochen Zha ◽  
Kwei-Herng Lai ◽  
Songyi Huang ◽  
Yuanpu Cao ◽  
Keerthana Reddy ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Research And Development ◽  
Imperfect Information ◽  
State Representation ◽  
Research Opportunities ◽  
Card Games ◽  
Imperfect Information Games ◽  
Learning Research ◽  
Information Games

We present RLCard, a Python platform for reinforcement learning research and development in card games. RLCard supports various card environments and several baseline algorithms with unified easy-to-use interfaces, aiming at bridging reinforcement learning and imperfect information games. The platform provides flexible configurations of state representation, action encoding, and reward design. RLCard also supports visualizations for algorithm debugging. In this demo, we showcase two representative environments and their visualization results. We conclude this demo with challenges and research opportunities brought by RLCard. A video is available on YouTube.

Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes

2021 ◽  
Author(s):  
Yun Zhang ◽  
Ling Wang ◽  
Xinqiao Wang ◽  
Chengyun Zhang ◽  
Jiamin Ge ◽  
...  
Keyword(s):  
Organic Chemistry ◽  
Deep Learning ◽  
Drug Discovery ◽  
Research And Development ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Chemical Reactions ◽  
Data Augmentation ◽  
Learning Method ◽  
Reaction Prediction

An effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery.

Deep Learning-Based Ground Vibration Monitoring: Reinforcement Learning and RNN-CNN Approach

2021 ◽  
pp. 1-5
Author(s):  
Sangseok Yun ◽  
Jae-Mo Kang ◽  
Jeongseok Ha ◽  
Sangho Lee ◽  
Dong-Woo Ryu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Ground Vibration ◽  
Vibration Monitoring

scholarly journals Diversity oriented Deep Reinforcement Learning for targeted molecule generation

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Tiago Pereira ◽  
Maryam Abbasi ◽  
Bernardete Ribeiro ◽  
Joel P. Arrais
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Deep Neural Networks ◽  
Chemical Space ◽  
Biological Properties ◽  
Training Process ◽  
Training Strategy ◽  
Inhibitory Power ◽  
Exploratory Strategy

AbstractIn this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with interesting biological properties. Two deep neural networks compose our targeted generation framework: the Generator, which is trained to learn the building rules of valid molecules employing SMILES strings notation, and the Predictor which evaluates the newly generated compounds by predicting their affinity for the desired target. Then, the Generator is optimized through Reinforcement Learning to produce molecules with bespoken properties. The innovation of this approach is the exploratory strategy applied during the reinforcement training process that seeks to add novelty to the generated compounds. This training strategy employs two Generators interchangeably to sample new SMILES: the initially trained model that will remain fixed and a copy of the previous one that will be updated during the training to uncover the most promising molecules. The evolution of the reward assigned by the Predictor determines how often each one is employed to select the next token of the molecule. This strategy establishes a compromise between the need to acquire more information about the chemical space and the need to sample new molecules, with the experience gained so far. To demonstrate the effectiveness of the method, the Generator is trained to design molecules with an optimized coefficient of partition and also high inhibitory power against the Adenosine $$A_{2A}$$ A 2 A and $$\kappa$$ κ opioid receptors. The results reveal that the model can effectively adjust the newly generated molecules towards the wanted direction. More importantly, it was possible to find promising sets of unique and diverse molecules, which was the main purpose of the newly implemented strategy.

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