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.

scholarly journals A Novel Model-Based Reinforcement Learning Attitude Control Method for Virtual Reality Satellite

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jian Zhang ◽  
Fengge Wu
Keyword(s):  
Virtual Reality ◽  
Reinforcement Learning ◽  
Real World ◽  
Attitude Control ◽  
Mixed Model ◽  
Control Method ◽  
Sensor Data ◽  
Dynamics Model ◽  
Goal State ◽  
The Impact

Observing the universe with virtual reality satellite is an amazing experience. An intelligent method of attitude control is the core object of research to achieve this goal. Attitude control is essentially one of the goal-state reaching tasks under constraints. Using reinforcement learning methods in real-world systems faces many challenges, such as insufficient samples, exploration safety issues, unknown actuator delays, and noise in the raw sensor data. In this work, a mixed model with different input sizes was proposed to represent the environmental dynamics model. The predication accuracy of the environmental dynamics model and the performance of the policy trained in this paper were gradually improved. Our method reduces the impact of noisy data on the model’s accuracy and improves the sampling efficiency. The experiments showed that the agent trained with our method completed a goal-state reaching task in a real-world system under wireless circumstances whose actuators were reaction wheels, whereas the soft actor-critic method failed in the same training process. The method’s effectiveness is ensured theoretically under given conditions.

Direct Unsupervised Super-Resolution using Generative Adversarial Network (DUS-GAN) for Real-World Data

2021 ◽  
pp. 1-1
Author(s):  
Kalpesh Prajapati ◽  
Vishal Chudasama ◽  
Heena Patel ◽  
Kishor Upla ◽  
Kiran Raja ◽  
...  
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Real World Data ◽  
Generative Adversarial Network ◽  
World Data ◽  
Adversarial Network

scholarly journals Using the Generative Adversarial Network to Generate Recommendations

2020 ◽  
Author(s):  
A.V. Prosvetov
Keyword(s):  
Real World ◽  
Recommendation System ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Theoretical Comparison ◽  
New Methods ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Recommendation Algorithms ◽  
Cold Start Problem

Widely used recommendation systems do not meet all industry requirements, so the search for more advanced methods for creating recommendations continues. The proposed new methods based on Generative Adversarial Networks (GAN) have a theoretical comparison with other recommendation algorithms; however, real-world comparisons are needed to introduce new methods in the industry. In our work, we compare recommendations from the Generative Adversarial Network with recommendation from the Deep Semantic Similarity Model (DSSM) on real-world case of airflight tickets. We found a way to train the GAN so that users receive appropriate recommendations, and during A/B testing, we noted that the GAN-based recommendation system can successfully compete with other neural networks in generating recommendations. One of the advantages of the proposed approach is that the GAN training process avoids a negative sampling, which causes a number of distortions in the final ratings of recommendations. Due to the ability of the GAN to generate new objects from the distribution of the training set, we assume that the Conditional GAN is able to solve the cold start problem.

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 Asymmetric Encoder-Decoder Structured FCN Based LiDAR to Color Image Generation

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4818 ◽  
Author(s):  
Hyun-Koo Kim ◽  
Kook-Yeol Yoo ◽  
Ju H. Park ◽  
Ho-Youl Jung
Keyword(s):  
Color Image ◽  
Ground Truth ◽  
Sensor Data ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Adversarial Network ◽  
Ground Truth Image ◽  
And Performance ◽  
Reflection Intensity

In this paper, we propose a method of generating a color image from light detection and ranging (LiDAR) 3D reflection intensity. The proposed method is composed of two steps: projection of LiDAR 3D reflection intensity into 2D intensity, and color image generation from the projected intensity by using a fully convolutional network (FCN). The color image should be generated from a very sparse projected intensity image. For this reason, the FCN is designed to have an asymmetric network structure, i.e., the layer depth of the decoder in the FCN is deeper than that of the encoder. The well-known KITTI dataset for various scenarios is used for the proposed FCN training and performance evaluation. Performance of the asymmetric network structures are empirically analyzed for various depth combinations for the encoder and decoder. Through simulations, it is shown that the proposed method generates fairly good visual quality of images while maintaining almost the same color as the ground truth image. Moreover, the proposed FCN has much higher performance than conventional interpolation methods and generative adversarial network based Pix2Pix. One interesting result is that the proposed FCN produces shadow-free and daylight color images. This result is caused by the fact that the LiDAR sensor data is produced by the light reflection and is, therefore, not affected by sunlight and shadow.

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.

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