self.gamma =0.95# discount rateself.epsilon =1.0# exploration rateself.epsilon_min =0.01self.epsilon_decay =0.995self.learning_rate =0.005self.model = self._build_model()def_build_model(self):# Neural Net for Deep-Q learning Modelmodel = tf.keras.models.Sequential() model.add(tf.keras.lay...
本文搬运自如下连接:Deep Q-Learning with Keras and Gym · Keon's Blog什么是强化学习?强化学习是机器学习的一种类型,通过创建Agent并与环境互动来学习。
This paper opened the era of what is called ‘deep reinforcement learning’,a mix of deep learing and reinforcement learning. Then, 通过实践了解这个牛牛的网络:Deep Q Learning with Keras and Gym 外加一个有良心的国内博客:用Tensorflow基于Deep Q Learning DQN 玩Flappy Bird(课外阅读) Cartpole Game ...
On the other hand, reinforcement learning approaches directly output the agent's action. References: Deep Q-Learning with Keras and Gym Double Deep Q Networks Using Keras and Deep Deterministic Policy Gradient to play TORCS Practical Deep Reinforcement Learning Approach for Stock Trading Introduction ...
Deep Q-network Keras implementation of DQN (DQN.ipynb) for MsPacman-v0 from OpenAI Gym. Implements Deep Q-network (DQN) in Keras following the architecture proposed in the 2013 paper by V. Mnih et al., "Playing Atari with Deep Reinforcement Learning": arXiv:1312.5602. See:http://www....
Reinforcement learning The multi-armed bandit Contextual bandits RL with the OpenAI Gym A Q-Learning model Markov decision process and the Bellman equation Q-learning Q-learning and exploration First DRL with Deep Q-learning RL experiments Keras RL Exercises Summary Unity ML-Agents Installing ML-Agen...
Keras Summary Chapter 9. Recommendation Systems Using Factorization Machines Recommendation systems Movie recommendation using collaborative filtering Factorization machines for recommendation systems Improved factorization machines Summary Chapter 10. Reinforcement Learning The RL problem OpenAI Gym The Q-Learning al...
Data Science: Deep Learning in Python Best Deep Learning books 2023 Deep Learning (Adaptive Computation and Machine Learning series) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems ...
This interpreter is loosely based around the OpenAI Gym environment [30], a standardized framework for RL and environment interaction and RL is implemented with the keras-rl python package [31]. The python PySWMM wrapper for SWMM allows for the SWMM model to be run incrementally, an essential...
This is a deep dive into deep reinforcement learning. We will tackle a concrete problem with modern libraries such as TensorFlow, TensorBoard, Keras, and OpenAI Gym. You will learn how to implement one of the fundamental algorithms called deep Q-learning