Answers.Body: <h1>Depends on perspective.</h1>
  <p>On one hand, you have an agent playing in an environment with another agent also evolving. This falls under the definition of Multi-Agent Learning, as can be seen with works such as</p>
  <ul>
  <li><p>Michael Bowling and Manuela Veloso. Multiagent learning using a variable learning
  rate. Artificial Intelligence, 136(2):215 – 250, 2002.</p>
  </li>
  <li><p>Michael Bowling. Convergence and no-regret in multiagent learning. In Proceedings of
  the 17th International Conference on Neural Information Processing Systems, NIPS’04,
  pages 209–216, Cambridge, MA, USA, 2004. MIT Press.</p>
  </li>
  <li><p>M. D. Awheda and H. M. Schwartz. Exponential moving average q-learning algorithm. In
  2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning
  (ADPRL), pages 31–38, April 2013.</p>
  </li>
  <li><p>Sherief Abdallah and Victor Lesser. A multiagent reinforcement learning algorithm with
  non-linear dynamics. Journal of Artificial Intelligence Research, 33:521–549, 2008.</p>
  </li>
  </ul>
  <p>However, you can also claim that you simply have a single agent learning on a non-stationary environment (the environment contains both the game rules and the opponent), and you simply learn on that basis. From this perspective, there is no multi-agent learning at all.</p>
  
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Answers.Text: Thanks for your answer. But could not one argue with the second argument, that all multi agent learning is just single agent learning, as long as the agents do not need to act cooperatively? I mean it's just an agent in an non stationary environment?
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Answers.Text: Yes, you could argue that any "fully competitive environment" can be considered a non-stationary single. Tbh I don't like this interpretation, it was a discussion I had during my PhD defense, but it is still a PoV
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Body: <p>Can AlphaZero considered as Multi-Agent Deep Reinforcement Learning?</p>
  <p>I could not find a clear answer on this. I would say yes it is Multi Agent Learning, as there are two Agents playing against each other.</p>
  
Tags: <deep-learning><deep-rl><multi-agent-systems><alphazero>
Title: Can AlphaZero considered as Multi-Agent Deep Reinforcement Learning?
UserDisplayName: flexw
ThreadText: # Can AlphaZero considered as Multi-Agent Deep Reinforcement Learning?
  
  (Asked by: username_0 on 2020-08-02)
  
  Can AlphaZero considered as Multi-Agent Deep Reinforcement Learning?
  
  I could not find a clear answer on this. I would say yes it is Multi Agent Learning, as there are two Agents playing against each other.
  
  **Tags:** deep-learning, deep-rl, multi-agent-systems, alphazero
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-08-07. Score: 3 | Accepted answer
  
  # Depends on perspective.
  
  On one hand, you have an agent playing in an environment with another agent also evolving. This falls under the definition of Multi-Agent Learning, as can be seen with works such as
  
  * Michael Bowling and Manuela Veloso. Multiagent learning using a variable learning
    rate. Artificial Intelligence, 136(2):215 – 250, 2002.
  * Michael Bowling. Convergence and no-regret in multiagent learning. In Proceedings of
    the 17th International Conference on Neural Information Processing Systems, NIPS’04,
    pages 209–216, Cambridge, MA, USA, 2004. MIT Press.
  * M. D. Awheda and H. M. Schwartz. Exponential moving average q-learning algorithm. In
    2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning
    (ADPRL), pages 31–38, April 2013.
  * Sherief Abdallah and Victor Lesser. A multiagent reinforcement learning algorithm with
    non-linear dynamics. Journal of Artificial Intelligence Research, 33:521–549, 2008.
  
  However, you can also claim that you simply have a single agent learning on a non-stationary environment (the environment contains both the game rules and the opponent), and you simply learn on that basis. From this perspective, there is no multi-agent learning at all.
  
  > **Comments:**
  > **username_0**: Thanks for your answer. But could not one argue with the second argument, that all multi agent learning is just single agent learning, as long as the agents do not need to act cooperatively? I mean it's just an agent in an non stationary environment?  
  > **username_1**: Yes, you could argue that any "fully competitive environment" can be considered a non-stationary single. Tbh I don't like this interpretation, it was a discussion I had during my PhD defense, but it is still a PoV  
  
  ---
  
  