Game Theory Concepts for Reinforcement Learning
There are many flavors of games in Game Theory which are interesting from Machine Learning perspectives, especially from multi-agent Reinforcement Learning applications. Here is the summary of multiple game types are if MinMax algorithm works and what type of strategy one needs to employ.
Comments on Strategy
Game ID#4: If strongly dominant strategy is present, then pure strategy might work or else might need a mixed strategy
Game ID#5: For small number of rounds (gamma~=0), betrayal might provide more reward, but for infinite number of rounds (gamm~=1), cooperation yields more reward
Interesting Facts from the Theory
- Tit-for-Tat is not subgame perfect when considering a longer future time horizon! It means it can give itself more rewards overall if it does not choose to retribute against the other player! So forgiveness is a better virtue! This forgiving strategy is called Pavlov state machine!