Body: <p>This is an Inverse Reinforcement Learning (IRL) problem. I have data (observations) on actions taken by a (real) agent. Given this data I want to estimate the likelihood of the observed actions in a Q-learning agent. Rewards are given by a linear function on a parameter, say alpha.</p>
  
  <p>Thus, I want to estimate the alpha that makes the observed actions more likely to be taken by a Q-agent. I read some papers (i.e. Ng &amp; Russel 2004), but I found them rather generalistic.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-04T20:59:53.240Z
Comments.Text: Hi and welcome to this community! This is an interesting post, but what is your question exactly?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-05T17:52:13.383Z
Comments.Text: HI, thanks! My question is how could I implement an IRL algorithm, like [Ng and Russell, 2000], to a Markov game where multi-agents have opposite interests. The game is simple though, as it is a  matrix game. I read some papers on IRL, but neither of them accounts for a multi-agent framework. Actually what I ask is for any ideas on how to approach to a problem of this nature.
Comments.UserDisplayName: Julian Lopez Baasch
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-10-27T21:54:36.127Z
Comments.Text: Have you looked into the work done by Deepmind on AlphaStar for StarCraft?
  They use a combination of different methods to achieve their goal - one of which is IRL. You can read more about their outline of implementation here: https://deepmind.com/blog/article/alphastar-mastering-real-time-strategy-game-starcraft-ii
Comments.UserDisplayName: Krrrl
Tags: <reinforcement-learning>
Title: Inverse Reinforcement Learning for Markov Games
UserDisplayName: Julian Lopez Baasch
ThreadText: # Inverse Reinforcement Learning for Markov Games
  
  (Asked by: username_0 on 2019-06-04)
  
  This is an Inverse Reinforcement Learning (IRL) problem. I have data (observations) on actions taken by a (real) agent. Given this data I want to estimate the likelihood of the observed actions in a Q-learning agent. Rewards are given by a linear function on a parameter, say alpha.
  
  Thus, I want to estimate the alpha that makes the observed actions more likely to be taken by a Q-agent. I read some papers (i.e. Ng & Russel 2004), but I found them rather generalistic.
  
  **Tags:** reinforcement-learning
  
  > **Comments:**
  > **username_1**: Hi and welcome to this community! This is an interesting post, but what is your question exactly?  
  > **username_0**: HI, thanks! My question is how could I implement an IRL algorithm, like [Ng and Russell, 2000], to a Markov game where multi-agents have opposite interests. The game is simple though, as it is a  matrix game. I read some papers on IRL, but neither of them accounts for a multi-agent framework. Actually what I ask is for any ideas on how to approach to a problem of this nature.  
  > **username_2**: Have you looked into the work done by Deepmind on AlphaStar for StarCraft? They use a combination of different methods to achieve their goal - one of which is IRL. You can read more about their outline of implementation here: https://deepmind.com/blog/article/alphastar-mastering-real-time-strategy-game-starcraft-ii  
  
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  ## 0 answers
  
  