这本书是介绍深度强化学习的,使用python,非常新,2020年出版的,761页,github有代码,貌似没有中文版。 介绍深度学习的书籍有很多,比如Richard Shutton的Reinforcement Learning, An Introduction, 2nd edition,作者是业界大牛,他的学生也是大牛。不过他属于value iteration流派,deep mind就
这是我的Github仓库：https://github.com/XinJingHao/Deep-Reinforcement-Learning-Algorithms-with-Pytorch...
这是我的Github仓库：https://github.com/XinJingHao/Deep-Reinforcement-Learning-Algorithms-with-Pytorch...
PyTorch implementations of deep reinforcement learning algorithms and environments - Yusics/Deep-Reinforcement-Learning-Algorithms-with-PyTorch
Understand the Markov Decision Process, Bellman’s optimality, and TD learning Solve multi-armed-bandit problems using various algorithms Master deep learning algorithms, such as RNN, LSTM, and CNN with applications Build intelligent agen... (展开全部) 我来说两句 短评 ··· ( 全部3 条 ) 热...
With significant enhancement in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been completely revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with Tens...
Installation and Set-Up for RL with Python and OpenAI´s Gymnasium Library Strengths and Weaknesses of RL (compared to ML and DL) RL Use Cases and Algorithms Understand and master Q-Learning and Q-Tables for complex RL projects Identify and manage RL Pitfalls such as Overfitting and Performa...
Reinforcement Learning in Python:实践指南,1.背景介绍人工智能(ArtificialIntelligence,AI)是一门研究如何让计算机自主地学习、理解、推理和决策的科学。人工智能的一个重要分支是强化学
Every chapter is accompanied by high-quality implementations based on the latest version of Python packages such as Gym, and the implementations of deep reinforcement learning algorithms are all with both TensorFlow 2 and PyTorch 1. All codes can be found on GitHub along with their results and ...
Implementing Q-learning in python Training the Agent First, we'll initialize the Q-table to a 500×6 matrix of zeros: import numpy as np q_table = np.zeros([env.observation_space.n, env.action_space.n]) Learn Data Science with We can now create the training algorithm that will ...