Python Deep Learning Projects

Training loop

Now, let's put all of the pieces we have formed until now together to implement training of the agent using the train() function that we have defined here:

1. Load the agent by calling the agent() function and compile it with the loss as loss and with the optimizer as optimizer, which we have defined in the Defining hyperparameters for Deep Q Learning (DQN) section.
2. Reset the environment and reshape the initial state.
3. Call the agent_action function by passing the model, epsilon, and state information and obtain the next action that needs to be taken.
4. Take the action obtained in Step 3 using the env.step function. Store the resulting information in the training_data deque container by calling the memory function and passing the ...