Sketch Generation From Real Object Images Using Generative Adversarial Network and Deep Reinforcement Learning

2021 ◽  
Author(s):  
Shintya Rezky Rahmayanti ◽  
Chastine Fatichah ◽  
Nanik Suciati
Keyword(s):  
Reinforcement Learning ◽  
Real Object ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Deep inverse reinforcement learning for structural evolution of small molecules

2020 ◽  
Author(s):  
Brighter Agyemang ◽  
Wei-Ping Wu ◽  
Daniel Addo ◽  
Michael Y Kpiebaareh ◽  
Ebenezer Nanor ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
High Throughput Screening ◽  
Structural Evolution ◽  
Search Space ◽  
New Drugs ◽  
Inverse Reinforcement Learning ◽  
Generative Adversarial Network ◽  
Entropy Maximization ◽  
Reward Function ◽  
Adversarial Network

Abstract The size and quality of chemical libraries to the drug discovery pipeline are crucial for developing new drugs or repurposing existing drugs. Existing techniques such as combinatorial organic synthesis and high-throughput screening usually make the process extraordinarily tough and complicated since the search space of synthetically feasible drugs is exorbitantly huge. While reinforcement learning has been mostly exploited in the literature for generating novel compounds, the requirement of designing a reward function that succinctly represents the learning objective could prove daunting in certain complex domains. Generative adversarial network-based methods also mostly discard the discriminator after training and could be hard to train. In this study, we propose a framework for training a compound generator and learn a transferable reward function based on the entropy maximization inverse reinforcement learning (IRL) paradigm. We show from our experiments that the IRL route offers a rational alternative for generating chemical compounds in domains where reward function engineering may be less appealing or impossible while data exhibiting the desired objective is readily available.

scholarly journals Deep reinforcement learning for diagnosing various types of cancer by TP53 mutation patterns

2021 ◽  
Author(s):  
Armaqan Rahmani ◽  
Behrouz Minaei-Bidgoli ◽  
Meysam Ahangaran
Keyword(s):  
Reinforcement Learning ◽  
Tp53 Mutation ◽  
Short Term Memory ◽  
Disease Risk ◽  
P53 Mutation ◽  
Multiple Cancer ◽  
Generative Adversarial Network ◽  
Cancer Subtypes ◽  
Adversarial Network ◽  
Cancer Types

Abstract One of the key challenges for classifying multiple cancer types is the complexity of Tumor Protein p53 mutation patterns and its individual effects on tumors. However, far too little attention has been paid to Deep reinforcement Learning on TP53 mutation patterns because of its extremely difficult result interpretations. We introduce a critic network by a long-short term memory, which is appropriated for discriminating the noise samples from a Feedback Generative Adversarial Network and analyzing the actor network. The correlation and analysis of the results in a belief network demonstrates significant relations between mutations and disease risk in cancer subtypes identification. In other words, the results indicate statically significant differences between the primary and secondary subtype groups of the most probable tumor.

scholarly journals A Method of Offline Reinforcement Learning Virtual Reality Satellite Attitude Control Based on Generative Adversarial Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jian Zhang ◽  
Fengge Wu
Keyword(s):  
Virtual Reality ◽  
Reinforcement Learning ◽  
Real World ◽  
Attitude Control ◽  
Control Method ◽  
Sensor Data ◽  
Generative Adversarial Network ◽  
Goal State ◽  
Safety Issues ◽  
Adversarial Network

Virtual reality satellites give people an immersive experience of exploring space. The intelligent attitude control method using reinforcement learning to achieve multiaxis synchronous control is one of the important tasks of virtual reality satellites. In real-world systems, methods based on reinforcement learning face safety issues during exploration, unknown actuator delays, and noise in the raw sensor data. To improve the sample efficiency and avoid safety issues during exploration, this paper proposes a new offline reinforcement learning method to make full use of samples. This method learns a policy set with imitation learning and a policy selector using a generative adversarial network (GAN). The performance of the proposed method was verified in a real-world system (reaction-wheel-based inverted pendulum). The results showed that the agent trained with our method reached and maintained a stable goal state in 10,000 steps, whereas the behavior cloning method only remained stable for 500 steps.

Multimodal News Feed Evaluation System with Deep Reinforcement Learning Approaches

2021 ◽  
Vol 20 (1) ◽  
pp. 1-12
Author(s):  
S. Rakesh Kumar ◽  
S. Muthuramalingam ◽  
Fadi Al-Turjman
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Evaluation System ◽  
Language Models ◽  
Learning Approaches ◽  
Generative Adversarial Network ◽  
Vast Number ◽  
News Analysis ◽  
Adversarial Network ◽  
Learning Techniques

Multilingual and multimodal data analysis is the emerging news feed evaluation system. News feed analysis and evaluations are interrelated processes, which are useful in understanding the news factors. The news feed evaluation system can be implemented for single or multilingual language models. Classification techniques used on multilingual news analysis require deep layered learning techniques rather than conventional approaches. In this proposed work, a hierarchical structure of deep learning algorithms is implemented for making an effective complex news evaluation system. Deep learning techniques such as the Deep Cooperative Multilingual Reinforcement Learning Model, the Multidimensional Genetic Algorithm, and the Multilingual Generative Adversarial Network are developed to evaluate a vast number of news feeds. The proposed tech-niques collaborate in a pipeline order to build a deep news feed evaluation system. The implementation details project that the newly proposed system performs 5% to 12% better than the other news evaluation systems.

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