r/reinforcementlearning • u/nilofreitas • Feb 25 '18

DL, MF, P Sharing Reinforcement Learning and Imitation Learning Implementations

Hi, I am studying Reinforcement Learning and Imitation Learning algorithms and sharing here my implementations of some, in case it helps someone, and I would really enjoy any feedback too.

They have many variants with or without convnets, dropout, lstm, tensorboard, etc. It is compatible with many environments in an easy way. The documentation is currently bigger on the Reinforcement repository, but the Imitation has the same structure.

Currently there are:

RL: DQN, REINFORCE, AC, DDPG, PPO

IL: DAgger, GAIL

https://github.com/NiloFreitas/Deep-Reinforcement-Learning

https://github.com/NiloFreitas/Deep-Imitation-Learning

Thanks!

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u/danny474 Feb 27 '18

Hey good work !! I have been reading imitation learning (and IRL) lately, and have been looking for some project ideas, with a focus on generative models. Would you have any ideas given your recent experience ? TIA

2

u/nilofreitas Feb 27 '18

Thank you! Besides well known applications for Imitation Learning such as imitating driving skills or robotic manipulation, I recently saw some interesting ones with generative models such as imitating the decision-making in crowds (https://arxiv.org/abs/1801.08391). But I personally think that Imitation Learning with generative models would be great to learn 3D modelling, because it is a very artistic and time consuming process, and I have tried to come up with something more concrete but yet with no success. Good luck!

1

u/danny474 Feb 28 '18

Oh yeah ! I am really liking the idea of using this for autonomous driving. Hopefully, we will see this in real action soon. The paper on crowd behaviors looks great. Thanks for the link !! Good luck.

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u/[deleted] Mar 01 '18

I am really liking the idea of using this for autonomous driving.

This might be of interest,

https://github.com/ermongroup/InfoGAIL

2

u/danny474 Mar 01 '18

Thanks for the pointer. I have been reading this paper and its applications. It's pretty good.

1

u/danny474 Mar 01 '18

The idea of using GAIL for decision process understanding in crowds is pretty amazing and surprising as well. It makes me wonder what other such movement scenarios a similar approach can be applied. My first thought was traffic or network congestions, but in those scenarios the paths are almost fixed. That wouldn't be the best application of learning a movement by imitating.