I'm working on my thesis concerning a reinforcement learning problem and am trying to prioritise my time on different components of it:
- Formalising the agent environment (like the design of state-, action-space and reward-structure)
- Selection of learning algorithm
- Selection of network architecture and size
- Design of the training setup
It is an agent in a 3D environment with simulated physics (in Unity), the domain being a real-time strategical game. It is an environment with constraint training data, so sample efficiency is very important.
Now my question: I do anticipate that the design of the state- and action space will have a big impact on the training result, especially in this environment with little training data.
However, is there a way one can clearly prioritise what components will be the most important ones for an RL setting?
Time is limited, and, for me, as a beginner, it seems to be quite difficult to determine what component will be the most important one and needs the most focus. Testing only the hyper-parameters of a learning algorithm thoroughly will take in itself a long time. And obviously disregarding any component will result in bad results.
Is there a way to know on which component one should focus more?