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, ...
