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README.md
actor_critic_network.py
basic_q_agent.py
deep_q_agent.py
option_critic_network.py

README.md

Time Skip Reinforcement Learning

Prior work:

https://arxiv.org/abs/1605.05365

This paper does something very similar, however their model adds the dynamic duration by adding a second version of each action with a different duration. I would add a second decision (either within the same model or with a second, parallel model) which selects the duration over which to perform the chosen action.

ftp://ftp.cs.utexas.edu/pub/neural-nets/papers/braylan.aaai15.pdf

Explores use of very large (but static) frame-skip values and discovers that on some games they deliver very good results.

https://danieltakeshi.github.io/2016/11/25/frame-skipping-and-preprocessing-for-deep-q-networks-on-atari-2600-games/ Explanation of the motivation and mechanism behind skipping frames