Maximum Entropy Reinforcement Learning (Stochastic Control)Dongmin Lee
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I reviewed the following papers.
- T. Haarnoja, et al., ¡°Reinforcement Learning with Deep Energy-Based Policies", ICML 2017
- T. Haarnoja, et al., ¡°Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor", ICML 2018
- T. Haarnoja, et al., ¡°Soft Actor-Critic Algorithms and Applications", arXiv preprint 2018
Thank you.
A Multi-Armed Bandit Framework For Recommendations at NetflixJaya Kawale
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In this talk, we present a general multi-armed bandit framework for recommendations on the Netflix homepage. We present two example case studies using MABs at Netflix - a) Artwork Personalization to recommend personalized visuals for each of our members for the different titles and b) Billboard recommendation to recommend the right title to be watched on the Billboard.
The guided policy search(GPS) is the branch of reinforcement learning developed for real-world robotics, and its utility is substantiated along many research. This slide show contains the comprehensive concept of GPS, and the detail way to implement, so it would be helpful for anyone who want to study this field.
This presentation provides an overview of artificial intelligence (AI) and deep learning. It begins with introductions to AI and deep learning, explaining that AI allows machines to perform tasks typically requiring human intelligence through machine learning. Deep learning is a type of machine learning using artificial neural networks inspired by the human brain. The presentation then discusses why AI has grown recently, citing increased computing power, data storage, and data availability. It also covers deep learning model development and concepts like underfitting and overfitting. The presentation describes different types of learning approaches like supervised, unsupervised, and reinforcement learning. It concludes with popular applications of deep learning like precision agriculture, computer vision, and recommendations.
A Multi-Armed Bandit Framework For Recommendations at NetflixJaya Kawale
?
In this talk, we present a general multi-armed bandit framework for recommendations on the Netflix homepage. We present two example case studies using MABs at Netflix - a) Artwork Personalization to recommend personalized visuals for each of our members for the different titles and b) Billboard recommendation to recommend the right title to be watched on the Billboard.
The guided policy search(GPS) is the branch of reinforcement learning developed for real-world robotics, and its utility is substantiated along many research. This slide show contains the comprehensive concept of GPS, and the detail way to implement, so it would be helpful for anyone who want to study this field.
This presentation provides an overview of artificial intelligence (AI) and deep learning. It begins with introductions to AI and deep learning, explaining that AI allows machines to perform tasks typically requiring human intelligence through machine learning. Deep learning is a type of machine learning using artificial neural networks inspired by the human brain. The presentation then discusses why AI has grown recently, citing increased computing power, data storage, and data availability. It also covers deep learning model development and concepts like underfitting and overfitting. The presentation describes different types of learning approaches like supervised, unsupervised, and reinforcement learning. It concludes with popular applications of deep learning like precision agriculture, computer vision, and recommendations.
[???] Multiagent Bidirectional- Coordinated Nets for Learning to Play StarCra...Kiho Suh
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The document summarizes a presentation on a paper about using multiagent bidirectional-coordinated networks (BiCNet) to develop AI agents that can learn to play combat games in StarCraft. The paper introduces BiCNet, which uses bidirectional RNNs to allow agents to communicate and coordinate their actions. Experiments show BiCNet agents outperform independent and other cooperative agents in different combat scenarios in StarCraft, developing strategies like focus firing and coordinated attacks. Visualizations of agent coordination and additional areas for investigation are also discussed.
Imagination-Augmented Agents for Deep Reinforcement Learning?? ?
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I will introduce a paper about I2A architecture made by deepmind. That is about Imagination-Augmented Agents for Deep Reinforcement Learning
This slide were presented at Deep Learning Study group in DAVIAN LAB.
Paper link: https://arxiv.org/abs/1707.06203