Keras Reinforcement Learning Projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents
About this ebook
- Build projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into action
- Get to grips with Keras and practice on real-world unstructured datasets
- Uncover advanced deep learning algorithms such as Monte Carlo, Markov Decision, and Q-learning
- Practice the Markov decision process in prediction and betting evaluations
- Implement Monte Carlo methods to forecast environment behaviors
- Explore TD learning algorithms to manage warehouse operations
- Construct a Deep Q-Network using Python and Keras to control robot movements
- Apply reinforcement concepts to build a handwritten digit recognition model using an image dataset
- Address a game theory problem using Q-Learning and OpenAI Gym
Keras Reinforcement Learning Projects is for you if you are data scientist, machine learning developer, or AI engineer who wants to understand the fundamentals of reinforcement learning by developing practical projects. Sound knowledge of machine learning and basic familiarity with Keras is useful to get the most out of this book
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About the author
Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli". He has over 25 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in MATLAB, Python and R. As an expert in AI applications to acoustics and noise control problems, Giuseppe has wide experience in researching and teaching. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He was recently included in the world's top 2% scientists list by Stanford University (2022).
Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.
Suriyadeepan Ramamoorthy is a machine learning engineer from Puducherry. His research focuses on interpretability, uncertainty, and reasoning. At Saama Research Lab, he applies NLU and reinforcement learning techniques, to optimize the clinical trial process. He actively blogs about advances in deep learning. He is a free-software evangelist who is involved in community development activities at FSHM, Puducherry. Community networks, data visualization, and creative coding are some of his other notable pursuits.
