Titlebook: Deep Reinforcement Learning with Python; With PyTorch, Tensor Nimish Sanghi Book 20211st edition Nimish Sanghi 2021 Artificial Intelligence

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What Is the Microsoft HoloLens?e two steps were carried out in a loop again and again until no further improvement in values was observed. In this chapter, we will look at a different approach for learning optimal policies by directly operating in the policy space. We will improve the policies without explicating learning or using state or state-action values.

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Introduction to Reinforcement Learning,as led to many significant advances that are increasingly getting machines closer to acting the way humans do. In this book, we will start with the basics and finish up with mastering some of the most recent developments in the field. There will be a good mix of theory (with minimal mathematics) and

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Markov Decision Processes,ic processes under the branch of probability that models sequential decision-making behavior. While most of the problems we study in reinforcement learning are modeled as . (MDP), we start by first introducing Markov chains (MC) followed by Markov reward processes (MRP). We finish up by discussing M

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Deep Q-Learning,learning using neural networks is also known as . (DQN). We will first summarize what we have talked about so far with respect to Q-learning. We will then look at code implementations of DQN on simple problems followed by training an agent to play Atari games. Following this, we will extend our know

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Policy Gradient Algorithms, a given current policy. In a second step, these estimated values were used to find a better policy by choosing the best action in a given state. These two steps were carried out in a loop again and again until no further improvement in values was observed. In this chapter, we will look at a differe