Closed brohrer closed 9 years ago
A prioritized (and highly plastic) list of subtasks.
An updated list of subtasks.
An updated list of subtasks.
An updated list of subtasks.
An updated list of subtasks.
An updated list of subtasks.
This task is being put on hold. Debugging it exposed that BECCA is in need of some fundamental reworking in order to be successful in this world. When dealing with a dynamic environment it is more effective to separate out the modeling of the world dynamics (state-action->state) from the assignment of value (state->reward). Traditional reinforcement learning cuts this corner by creating value functions of the form (state-action->reward). In the approach I'm pursuing this doesn't appear to scale, even with automatic feature creation. For all its simplicity, the chase world has far more possible states than a chess board.
Another benefit of separating out modeling from reward assignment is that it allows for a planner to intervene and specify intermediate goals by assigning temporary artificial reward values to arbitrary features. This sounds like something a human brain might do.
When I get BECCA back up and running, I'll revisit the chase world development and complexification.
Make more complex, more intuitive, more aesthetically appealing. Make world bigger. Incorporate aspects that require deep learning.