Neural model of hierarchical reinforcement learning
This code instantiates the model described in
Rasmussen, D., Voelker, A., & Eliasmith, C. (2017). A neural model of hierarchical reinforcement learning. PLoS ONE
Rasmussen, D. (2014). Hierarchical reinforcement learning in a biologically plausible neural architecture. University of Waterloo.
Rasmussen, D., & Eliasmith, C. (2014). A neural model of hierarchical reinforcement learning. Proceedings of the 36th Annual Conference of the Cognitive Science Society
Rasmussen, D., & Eliasmith, C. (2013). A neural reinforcement learning model for tasks with unknown time delays. Proceedings of the 35th Annual Conference of the Cognitive Science Society
This code relies on Nengo version 1.4, which can be downloaded here. Extract Nengo into a location of your choice, which we will call
Then check out this repository into a different folder
cd nhrl git clone https://github.com/drasmuss/nhrlmodel.git
The model can then be run through Nengo:
<nengo>/nengo-cl <nhrl>/hrlproject/misc/run.py delivery
delivery can be swapped for various keywords to run the model in different environments (see
The model is written in Python, and uses Jython to interact with the Java-based Nengo 1.4. Hence the code style is a bit of a mix of Java and Python conventions depending on where the code originated, which the reader will have to pardon.