Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. Keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Keras-rl - Deep Reinforcement Learning for Keras. You will also learn about recent advancements in reinforcement learning such as imagination augmented agents, learn from human preference, DQfD, HER and many more. Dueling DQN, DRQN, A3C, DDPG, TRPO, and PPO. You will master various deep reinforcement learning algorithms such as DQN, Double DQN. You will then explore deep reinforcement learning in depth, which is a combination of deep learning and reinforcement learning. This example-rich guide will introduce you to deep learning, covering various deep learning algorithms. You will then explore various RL algorithms and concepts such as the Markov Decision Processes, Monte-Carlo methods, and dynamic programming, including value and policy iteration. The book starts with an introduction to Reinforcement Learning followed by OpenAI and Tensorflow. Reinforcement Learning with Python will help you to master basic reinforcement learning algorithms to the advanced deep reinforcement learning algorithms. Hands-On-Reinforcement-Learning-With-Python - Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow
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