Answers.Body: <p>What you are describing is a <a href="https://arxiv.org/pdf/1707.00183.pdf" rel="nofollow noreferrer">teacher-student framework</a> in which you have one agent (the teacher) who determines the complexity of the task that another agent (the student) must accomplish. If the task is too difficult for the student to accomplish the teacher receives little to no reward and therefore changes the environment (the game) making it easier for the student to complete it.</p>
  <p>The paper <a href="https://arxiv.org/abs/1705.06366" rel="nofollow noreferrer">Automatic Goal Generation for Reinforcement Learning Agents</a> is quite interesting because it applies this concept using a Generative Adversarial Network (GAN). Here the generator network is the teacher and the adversarial network is the student trying to solve the task created by the generator network. The results in the paper demonstrate that no prior knowledge of the environment is required for the student to solve it and it can even be done with sparse reward, i.e., 1 for winning the game and 0 otherwise.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-03-03T07:13:34.230Z
Answers.LastActivityDate: 2022-03-03T07:13:34.230Z
Answers.UserDisplayName: Lars
Body: <ol>
  <li><p>If the agent wins the game, the agent is rewarded</p>
  </li>
  <li><p>If the agent wins the game, the game is rewarded</p>
  </li>
  <li><p>Both the agent and the game are reinforcement learning agents.</p>
  </li>
  </ol>
  <p>When the agent wins the game, the game is rewarded, so the game creates various game winning conditions.</p>
  <p>As the agent satisfies various game winning conditions, the reward increases, so the agent develops to win various games.</p>
  <p>An agent that can win various games develops into a general-purpose intelligence</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-03-03T08:44:37.287Z
Comments.Text: Your question will lead to opinions. What do you mean by "valid"? Also, why do I think that you're the same person asking the same questions again and again, always the same question about games that lead to AGI or games that are evolved? It's annoying. Stop spamming our site with these poor questions.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-03-23T11:35:26.590Z
Comments.Text: isn't the optimal behaviour for the game "always win" and for the agent "do whatever"?
Comments.UserDisplayName: Criticize SE actions means ban
Tags: <reinforcement-learning>
Title: Do you think this method of creating AI is valid?
UserDisplayName: Display name
ThreadText: # Do you think this method of creating AI is valid?
  
  (Asked by: username_0 on 2022-03-03)
  
  1. If the agent wins the game, the agent is rewarded
  2. If the agent wins the game, the game is rewarded
  3. Both the agent and the game are reinforcement learning agents.
  
  When the agent wins the game, the game is rewarded, so the game creates various game winning conditions.
  
  As the agent satisfies various game winning conditions, the reward increases, so the agent develops to win various games.
  
  An agent that can win various games develops into a general-purpose intelligence
  
  **Tags:** reinforcement-learning
  
  > **Comments:**
  > **username_1**: Your question will lead to opinions. What do you mean by "valid"? Also, why do I think that you're the same person asking the same questions again and again, always the same question about games that lead to AGI or games that are evolved? It's annoying. Stop spamming our site with these poor questions.  
  > **username_2**: isn't the optimal behaviour for the game "always win" and for the agent "do whatever"?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2022-03-03. Score: 1 | Accepted answer
  
  What you are describing is a [teacher-student framework](https://arxiv.org/pdf/1707.00183.pdf) in which you have one agent (the teacher) who determines the complexity of the task that another agent (the student) must accomplish. If the task is too difficult for the student to accomplish the teacher receives little to no reward and therefore changes the environment (the game) making it easier for the student to complete it.
  
  The paper [Automatic Goal Generation for Reinforcement Learning Agents](https://arxiv.org/abs/1705.06366) is quite interesting because it applies this concept using a Generative Adversarial Network (GAN). Here the generator network is the teacher and the adversarial network is the student trying to solve the task created by the generator network. The results in the paper demonstrate that no prior knowledge of the environment is required for the student to solve it and it can even be done with sparse reward, i.e., 1 for winning the game and 0 otherwise.
  
  
  ---
  
  