<p>Yes, evolutionary algorithms (EAs) can be used to solve/play games too. For example, OpenAI has used <a href="http://rnowotniak.kis.p.lodz.pl/quantum/Essentials%20of%20Metaheuristics.pdf#page=33" rel="nofollow noreferrer">evolution strategies</a> (a subset of EAs that uses fixed-length real-valued vectors and self-adaptive mutation rates) to play Atari games. In <a href="https://openai.com/blog/evolution-strategies/" rel="nofollow noreferrer">this blog post</a>, they write</p>
<blockquote>
<p>We've discovered that evolution strategies (ES), an optimization technique that's been known for decades, rivals the performance of standard reinforcement learning (RL) techniques on modern RL benchmarks (e.g. Atari/MuJoCo), while overcoming many of RL's inconveniences.</p>
</blockquote>
<p>There is also the related paper <a href="https://arxiv.org/abs/1703.03864" rel="nofollow noreferrer">Evolution Strategies as a Scalable Alternative to Reinforcement Learning</a> (2017) and <a href="https://github.com/openai/evolution-strategies-starter" rel="nofollow noreferrer">code</a>.</p>

CC BY-SA 4.0
2021-01-30T20:37:45.140Z
2021-01-30T20:47:34.573Z
2021-01-30T20:47:34.573Z
nbro
<p>As I see it, it all comes down to game theory, which can be said to form the foundation of successful decision making, and is particularly useful in a context, such as computing, where all parameters can be defined.  (Where it runs into trouble is with the aggregate complexity of the parameters per the <a href="http://cswww.essex.ac.uk/CSP/ComputationalFinanceTeaching/CombinatorialExplosion.html" rel="nofollow noreferrer">combinatorial explosion</a>, although Machine Learning has recently been validated as a method of managing intractability specifically in the context of games.)</p>
<p>You might want to check out <a href="https://core.ac.uk/download/pdf/205529758.pdf" rel="nofollow noreferrer">Playing Games with Genetic Algorithms</a> and <a href="https://en.wikipedia.org/wiki/Evolutionary_game_theory" rel="nofollow noreferrer">Evolutionary game theory</a>.</p>

CC BY-SA 4.0
2017-01-25T19:44:29.507Z
2021-01-30T19:35:28.770Z
2021-01-30T19:35:28.770Z
DukeZhou
<p>OpenAI's Universe utilizes RL algorithms. I also know that Q-learning has been used to solve some games.</p>
<p>Are there any other ML approaches to solve games? For example, could we use genetic algorithms to develop agents that solve games?</p>

<machine-learning><reinforcement-learning><genetic-algorithms><algorithm-request><model-request>
Apart from Reinforcement Learning, are there any other machine learning approaches to play video games?
U754V
# Apart from Reinforcement Learning, are there any other machine learning approaches to play video games?

(Asked by: username_0 on 2017-01-25)

OpenAI's Universe utilizes RL algorithms. I also know that Q-learning has been used to solve some games.

Are there any other ML approaches to solve games? For example, could we use genetic algorithms to develop agents that solve games?

**Tags:** machine-learning, reinforcement-learning, genetic-algorithms, algorithm-request, model-request


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## 2 answers

### Answer by username_2 on 2017-01-25. Score: 6 | Accepted answer

As I see it, it all comes down to game theory, which can be said to form the foundation of successful decision making, and is particularly useful in a context, such as computing, where all parameters can be defined. (Where it runs into trouble is with the aggregate complexity of the parameters per the [combinatorial explosion](http://cswww.essex.ac.uk/CSP/ComputationalFinanceTeaching/CombinatorialExplosion.html), although Machine Learning has recently been validated as a method of managing intractability specifically in the context of games.)

You might want to check out [Playing Games with Genetic Algorithms](https://core.ac.uk/download/pdf/205529758.pdf) and [Evolutionary game theory](https://en.wikipedia.org/wiki/Evolutionary_game_theory).


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### Answer by username_1 on 2021-01-30. Score: 0

Yes, evolutionary algorithms (EAs) can be used to solve/play games too. For example, OpenAI has used [evolution strategies](http://rnowotniak.kis.p.lodz.pl/quantum/Essentials%20of%20Metaheuristics.pdf#page=33) (a subset of EAs that uses fixed-length real-valued vectors and self-adaptive mutation rates) to play Atari games. In [this blog post](https://openai.com/blog/evolution-strategies/), they write

> We've discovered that evolution strategies (ES), an optimization technique that's been known for decades, rivals the performance of standard reinforcement learning (RL) techniques on modern RL benchmarks (e.g. Atari/MuJoCo), while overcoming many of RL's inconveniences.

There is also the related paper [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://arxiv.org/abs/1703.03864) (2017) and [code](https://github.com/openai/evolution-strategies-starter).


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