![]() ![]() A Springer textbook is also provided, you can get the free PDF if your institute has Springer license. ![]() Other large-scale distributed training framework for more realistic scenarios with Unity 3D, Mujoco, Bullet Physics, etc, will be supported in the future. ![]() Moreover, RLzoo supports robot learning benchmark environment RLBench based on Vrep/Pyrep simulator. It supports basic toy-tests like OpenAI Gym and DeepMind Control Suite with very simple configurations. It is implemented with Tensorflow 2.0 and API of neural network layers in TensorLayer 2, to provide a hands-on fast-developing approach for reinforcement learning practices and benchmarks. RLzoo is a collection of the most practical reinforcement learning algorithms, frameworks and applications. RLzoo - A Comprehensive Reinforcement Learning Zoo for Simple Usage □ In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. You can use built-in Keras callbacks and metrics or define your own. Of course you can extend keras-rl according to your own needs. This means that evaluating and playing around with different algorithms is easy. Furthermore, keras-rl works with OpenAI Gym out of the box. 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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