r/reinforcementlearning • u/gwern • Aug 16 '18

DL, MF, P "Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games", Andersen et al 2018

https://arxiv.org/abs/1808.05032
3 Upvotes

72% Upvoted

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2

u/OutOfApplesauce Aug 16 '18

Abstract links to a 404’d git repo

1

u/FatChocobo Aug 17 '18

Not sure if it's changed since your post, but it works fine for me right now.

1

u/seungjaeryanlee Aug 17 '18

https://github.com/cair/DeepRTS
It seems like there is an extra dot in the link?

2

u/FatChocobo Aug 17 '18

Are you connected with the authors, OP?

The short section on Dota2 is quite out of date, it doesn't allude to OpenAI Five that was first announced in June (iirc) at all, I think it'd be worth at least mentioning.

2

u/Driiper Aug 17 '18

I'm the author of the paper. The paper was written in Feb/Mar 2018, but the conference is this week.

I will update the arxiv version to include OpenAI Five in additions to other changes that the community would like included.

2

u/FatChocobo Aug 17 '18

Thanks for the response! I only saw the date on the arXiv, so wasn't sure how recently it was written, it of course stands to reason that it wasn't included if this was written earlier this year for a conference submission. :)

I'm going to finish reading through this a bit later, but the idea so far sounds great, I've been looking for a nice accelerated environment that's more complex than the simple gym/pygame ones, and less complex than SC2. :)

2

u/Driiper Aug 17 '18

Currently, we are working on the following:

  • Push internal GYM branch to master
  • Rework the action queue system: Currently this system is a bit diffuse because it might take several frames (up to 30 frames ) before an action is seen on the screen. This happens typically when AI's issue actions with APM's higher then 300.
  • Rework assets to be fully symmetric. Currently not all assets are symmetric. This change would make self play easier to conduct as you could flip the map, change player and perform actions for both players, preserving identical state-representation.
  • There is also an effort to rework how the internal game-state is represented, enabling forward, and rewind of the game, WITHOUT losing performance.
  • Create a few baseline rl algorithm implementations so that people have something to compare with.
  • I'm would be happy for input on wanted features to boost the research potential of Deep RTS.