# Similarities and differences between UCT algorithms in (i), (ii), (iii) and (iv)?

I am trying to understand the similarities and differences between: (i) the UCT algorithm in Kocsis and Szepesvári (2006); (ii) the UCT algorithm in Section 3.3 of Browne et al (2012); (iii) the MCTS algorithm in Silver et al. (2016); (iv) the MCTS algorithm in Silver et al. (2017).

I would be really grateful for some help identifying the similarities and differences in these papers, I am doing some research and really struggling right now.

(ii) http://mcts.ai/pubs/mcts-survey-master.pdf (Section 3.3)

The paper by Kocsis and Szepesvári from 2006 (your first link) is (one of) the original publication(s) on UCT. It is very similar to the "standard" implementation as described in the survey paper. If I recall correctly, the only important difference is that the algorithm as described in 2006 keeps track of at which point in time during an episode a reward is observed, and accounts for that timing in the backpropagation phase (i.e. if a reward is observed at time $$t$$ in an episode, credit for that reward is not assigned to states/action after time $$t$$). It also used a discounting factor $$\gamma$$ to discount the importance of reward depending on temporal distance, which is uncommon otherwise in MCTS literature.