Reinforcement Learning with TensorFlow

Deep Q-network for mountain car problem in OpenAI gym

We have already discussed the environment while implementing Q-learning for the mountain car problem. Let's dive directly into implementing a deep Q-network to solve the mountain car problem. First, we will import the required libraries, using the following code:

#importing the dependenciesimport numpy as npimport tensorflow as tfimport gym

Let's discuss our class DQN, which holds the architecture of the deep Q-network:

•  __init__(self, learning_rate, gamma, n_features, n_actions, epsilon, parameter_changing_pointer, memory_size): Default constructor to assign the hyperparameters such as:
  • learning_rate
  • gamma, that is, the discount factor
  • n_feature: Number of features in state, that is, ...