In adversarial games, the cumulative environment reward may not be a meaningful metric by which to track learning progress. This is because cumulative reward is entirely dependent on the skill of the opponent. An agent at a particular skill level will get more or less reward against a worse or better agent, respectively.
We provide an implementation of the ELO rating system, a method for calculating the relative skill level between two players from a given population in a zero-sum game. For more informtion on ELO, please see [the ELO wiki](https://en.wikipedia.org/wiki/Elo_rating_system).
We provide an implementation of the ELO rating system, a method for calculating the relative skill level between two players from a given population in a zero-sum game. For more information on ELO, please see [the ELO wiki](https://en.wikipedia.org/wiki/Elo_rating_system).
In a proper training run, the ELO of the agent should steadily increase. The absolute value of the ELO is less important than the change in ELO over training iterations.
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