<p>There is the <a href="https://github.com/openai/gym/wiki/Leaderboard" rel="nofollow noreferrer">leaderboard</a> page at the gym GitHub repository that contains links to specific implementations that &quot;solve&quot; the different gym environments, where &quot;to solve&quot; means &quot;to reach a certain level of performance&quot;, which, given a fixed reward function, is typically measured as the average (episodic) return/reward. For example, in the case of the CartPole environment, you solve it when you get an average reward of <span class="math-container">$195.0$</span> over <span class="math-container">$100$</span> consecutive trials.</p>

CC BY-SA 4.0
2020-10-31T18:37:10.433Z
2020-10-31T18:37:10.433Z
nbro
<p>What are the state-of-the-art results in OpenAI's gym environments? Is there a link to a paper/article that describes them and how these SOTA results were calculated?</p>

<reinforcement-learning><reference-request><gym><state-of-the-art>
What are the state-of-the-art results in OpenAI's gym environments?
Tofara Moyo
# What are the state-of-the-art results in OpenAI's gym environments?

(Asked by: username_0 on 2020-10-30)

What are the state-of-the-art results in OpenAI's gym environments? Is there a link to a paper/article that describes them and how these SOTA results were calculated?

**Tags:** reinforcement-learning, reference-request, gym, state-of-the-art


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## 1 answers

### Answer by username_1 on 2020-10-31. Score: 2

There is the [leaderboard](https://github.com/openai/gym/wiki/Leaderboard) page at the gym GitHub repository that contains links to specific implementations that "solve" the different gym environments, where "to solve" means "to reach a certain level of performance", which, given a fixed reward function, is typically measured as the average (episodic) return/reward. For example, in the case of the CartPole environment, you solve it when you get an average reward of $195.0$ over $100$ consecutive trials.


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