Niklas Riewald

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Reinforcement Learning

The math behind competitive Pokemon, Part 5: Game Tree pruning

In 6vs6 Pokemon games, you have up to 9 choices every turn: 4 moves and 5 possible switches. Sometimes there is also the option to mega evolve or use a Z-move which can increase possible options to 13. As your opponent has also ~9 choices to make, there are ~81 possible ways the next turn…

October 27, 2019 in AI, Math, Pokemon, Reinforcement Learning.

The math behind competitive Pokemon, Part 2: Value Function Approximation

The last post was about playing optimal Pokemon games by calculating the value function V(x) using Bellmann equations. Unfortunately, this was impractical, so we need another way to calculate the likelihood of winning games. One solution to this problem is called Value Function Approximation. The idea behind it is to not calculate the value function…

June 24, 2018 in Pokemon, Reinforcement Learning.

The math behind competitive Pokemon, Part 1: Bellman Equations

One year ago, I was drawn into the world of competitive Pokemon. It’s an incredibly deep game that requires quite an effort to master. First, you have to memorise a vast amount of information: Which type of attack is most effective against which type of Pokemon? What are the base stats of the different Pokemon,…

June 23, 2018 in Game Theory, Math, Pokemon, Reinforcement Learning.

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