Reinforcement Learning Algorithms with Python

Pseudocode

Now that all the components of DQN have been explained, we can put all the pieces together and show you the pseudocode version of the algorithm to clarify any uncertainties (don't worry if it doesn't – in the next section, you'll implement it and everything will be clearer).

The DQN algorithm involves three main parts:

• Data collection and storage. The data is collected by following a behavior policy (for example, -greedy).
• Neural network optimization (performing SGD on mini-batches that have been sampled from the buffer).
• Target update.

The pseudocode of DQN is as follows:

Initialize  function with random weight Initialize  function ...