Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-10-31T18:37:10.433Z
Answers.LastActivityDate: 2020-10-31T18:37:10.433Z
Answers.UserDisplayName: nbro
Body: <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>
  
Tags: <reinforcement-learning><reference-request><gym><state-of-the-art>
Title: What are the state-of-the-art results in OpenAI's gym environments?
UserDisplayName: Tofara Moyo
ThreadText: # 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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