Comparing multiple RL Algorithms

Using algos from robotics and finance to teach how to play tennis

Recently I tried out a very interesting PyTorch challenge: getting two robots to play tennis against each other, with agents from stock-trading.

Let’s review the agents we’re going to be using:

  1. Turtle-trading agent
  2. Moving-average agent
  3. Signal rolling agent
  4. Policy-gradient agent
  5. Q-learning agent
  6. Evolution-strategy agent
  7. Double Q-learning agent
  8. Recurrent Q-learning agent
  9. Double Recurrent Q-learning agent
  10. Duel Q-learning agent
  11. Double Duel Q-learning agent
  12. Duel Recurrent Q-learning agent
  13. Double Duel Recurrent Q-learning agent
  14. Actor-critic agent
  15. Actor-critic Duel agent
  16. Actor-critic Recurrent agent
  17. Actor-critic Duel Recurrent agent
  18. Curiosity Q-learning agent
  19. Recurrent Curiosity Q-learning agent
  20. Duel Curiosity Q-learning agent
  21. Neuro-evolution agent
  22. Neuro-evolution with Novelty search agent

Cited as:

@article{mcateer2018rlcomp,
  title   = "Comparing multiple RL Algorithms",
  author  = "McAteer, Matthew",
  journal = "matthewmcateer.me",
  year    = "2018",
  url     = "https://matthewmcateer.me/blog/comparing-multiple-rl-algorithms/"
}

If you notice mistakes and errors in this post, don’t hesitate to contact me at [contact at matthewmcateer dot me] and I would be very happy to correct them right away!

See you in the next post 😄

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