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@ -19,6 +19,8 @@ To pre-process the Atari-HEAD data run [Preprocess_AtariHEAD.ipynb](Preprocess_A
## Intention-based Hierarchical RL Agent ## Intention-based Hierarchical RL Agent
The Int-HRL agent is based on the hierarchically guided Imitation Learning method (hg-DAgger/Q), where we adapted code from [https://github.com/hoangminhle/hierarchical_IL_RL](https://github.com/hoangminhle/hierarchical_IL_RL) <br> The Int-HRL agent is based on the hierarchically guided Imitation Learning method (hg-DAgger/Q), where we adapted code from [https://github.com/hoangminhle/hierarchical_IL_RL](https://github.com/hoangminhle/hierarchical_IL_RL) <br>
![results](supplementary/results_updated.pdf)
Due to the novel sub-goal extraction pipeline, our agent does not require experts during training and is more than three times more sample efficient compared to hg-DAgger/Q. <br> Due to the novel sub-goal extraction pipeline, our agent does not require experts during training and is more than three times more sample efficient compared to hg-DAgger/Q. <br>
To run the full agent with 12 separate low-level agents for sub-goal execution, run [agent/run_experiment.py](agent/run_experiment.py), for single agents (one low-level agent for all sub-goals) run [agent/single_agent_experiment.py](agent/single_agent_experiment.py). To run the full agent with 12 separate low-level agents for sub-goal execution, run [agent/run_experiment.py](agent/run_experiment.py), for single agents (one low-level agent for all sub-goals) run [agent/single_agent_experiment.py](agent/single_agent_experiment.py).

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