Studying Machine Learning
Currently reading and studying Markov Decision Processes. 

After seeing search problems as a deterministic way to solve problems, we know that the world is not so certain, and here it comes the stochastic/randomness of the world. 

Ideally, a Markov decision process is pretty similar to a search problem, with some differences such as:

  • Set of states 
  • Set of actions that will help you go to one state to another. 
  • Transition model, as the probability that given you are in state s and perform action A you end up in state s’
  • Policy, is the recommended action in any state 
  • A reward function, is the reward you obtain  after transitioning from one state to another. 
There is more to it and I‘ll write more about it as I keep learning. 

It’s a fascinating topic and additionally the fundamentals of Reinforcement Learning