Reproduction of "Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation" by Kulkarni et al. (2016) in Python: https://arxiv.org/abs/1604.06057
This is a work in progress. I haven't been able to replicate the results yet.
Also, I haven't started on Montezuma's revenge yet. I intend to do this eventually, but I'm not sure when. Pull requests are welcomed and encouraged!
Comments/criticisms/suggestions/etc welcome, as always.
- Create MDP Environment [Done]
- Create a non-hierarchical actor-critic agent as a baseline [Done]
- Evaluate the non-hierachical actor-critic by plotting which states it visits [Done]
- Create a h-DQN agent [Done]
- Evaluate the h-DQN agent by plotting which states it visits [Done]
TODO (This might be a while. Pull requests welcome.)
Stochastic MDP Environment
The h-DQN agent is located in
./agent/hDQN.py. Below is our replication of Figure 4 from the paper: