Building Your Own Agent¶
In the previous section, we discussed the basic components of the
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:
agent.pyfile containing the high-level implementation of the
model.pyfile containing the PyTorch models appropriate for your chosen domain.
preset.pyfile that composes your
Agentusing the appropriate model and other objects.
main.pyor similar file that runs your agent and any
autonomous-learning-librarypresets 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.