Now, I know what you might be thinking: “Why would anyone want to do that? Isn’t it easier just to use the pre-trained models provided by the framework?” Well, let me tell ya something sometimes those pre-trained models aren’t good enough for your specific needs! Maybe they don’t have the same level of accuracy or maybe you need a model with more features. Whatever the reason may be, custom models are where it’s at.

So how do we go about creating our own custom models in maze RL frameworks? Well, first things first what kind of models we can use. There are two main types: feedforward and recurrent neural networks (RNNs). Feedforward NNs have a simple structure where the input is fed through multiple layers and then outputted at the end. RNNs, on the other hand, have a more complex structure that allows them to remember previous inputs and outputs in order to make better predictions for future inputs.

Now, how we can implement these models into our maze RL framework of choice (I personally prefer OpenAI Gym). First, you need to create your own environment class that extends the base Environment class provided by Gym. This will allow us to customize the behavior and functionality of our environment.

Next, how we can train our models using reinforcement learning techniques such as Q-learning or SARSA. These algorithms involve iteratively updating a set of weights (or parameters) in order to improve the performance of our model over time. The basic idea is that we start with some initial set of weights and then use them to predict an action for each state in our environment. We then receive feedback from the environment in the form of rewards, which are used to update our weights based on how well our predictions match up with reality.

Now, some specific examples of custom models that you can implement using OpenAI Gym and reinforcement learning techniques such as Q-learning or SARSA. One popular example is the Double DQN (DDQN) algorithm, which uses two separate networks to estimate both the action values (or Q-values) for each state and the target action values used during training. This helps to reduce overestimation errors that can occur when using a single network for both tasks.

Another example is the Dueling Network Architecture (DNA), which separates the value function into two separate networks: one for estimating the Q-values and another for estimating the advantage values (or A-values) used during training. This helps to improve the stability of our model by reducing the correlation between the estimated Q-values and the target action values, which can lead to overestimation errors when using a single network for both tasks.

It may seem daunting at first, but with a little bit of knowledge and some practice, anyone can create their own custom models that are tailored specifically to their needs. And who knows? Maybe one day your model will become the next big thing in reinforcement learning research!