Building Your Own Agent

In the previous section, we discussed the basic components of the autonomous-learning-library. While the library contains a selection of preset agents, the primary goal of the library is to be a tool to build your own agents. To this end, we have provided an example project containing a new model predictive control variant of DQN to demonstrate the flexibility of the library. Briefly, when creating your own agent, you will generally have the following components:

  1. An file containing the high-level implementation of the Agent.

  2. A file containing the PyTorch models appropriate for your chosen domain.

  3. A file that composes your Agent using the appropriate model and other objects.

  4. A or similar file that runs your agent and any autonomous-learning-library presets you wish to compare against.

While it is not necessary to follow this structure, we believe it will generally guide you towards using the autonomous-learning-library in the intended manner and ensure that your code is understandable to other users of the library.