4 Matching Annotations
  1. Jul 2023
    1. Paper that evaluated the existing Double Q-Learning algorithm on the new DQN approach and validated that it is very effective in the Deep RL realm.

    1. Liang, Machado, Talvite, Bowling - AAMAS 2016 "State of the Art Control of Atari Games Using Shallow Reinforcement Learning"

      Response paper to DQN showing that well designed Value Function Approximations can also do well at these complex tasks without the use of Deep Learning

      A great paper showing how to think differently about the latest advances in Deep RL. All is not always what it seems!

  2. Jun 2023
    1. Liang, Machado, Talvite, Bowling - AAMAS 2016 "State of the Art Control of Atari Games Using Shallow Reinforcement Learning"

      A great paper showing how to think differently about the latest advances in Deep RL. All is not always what it seems!