Gmargo11/hDQN: Implementation Of Hierarchical Deep Q ... - GitHub

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Name		Name	Last commit message	Last commit date
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__pycache__		__pycache__
agents		agents
envs		envs
utils		utils
README.md		README.md
paper.pdf		paper.pdf
presentation.pdf		presentation.pdf
run_tests.py		run_tests.py
run_tests_continuous.py		run_tests_continuous.py
run_tests_mdp.py		run_tests_mdp.py
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hDQN

Replication of the first experiment of Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation (Kulkarni et al., 2016) (view here).

Download the report here.

View the presentation here.

This work was done as a class project for MIT 6.882: Embodied Intelligence. Credit is due to Professor Tomas Lozano-Perez for providing valuable feedback on my approach. Credit is also due to a previous replication attempt of the hierarchical-DQN paper, which did not successfully replicate the results but inspired aspects of this implementation: https://github.com/EthanMacdonald/h-DQN

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Implementation of Hierarchical Deep Q-Learning (Kulkarni et al., 2016)

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