ML with Python - Issue with understanding the Markov Chain code

Hey everyone. As the title says, I’m having some trouble. I (think) I understand the way Abbey’s Markov chain works, and I’m trying to add one more step to it. Problem is that apparently, I at the very least don’t understand dictionaries well enough because I keep hitting the same problem and I don’t know why.

As you’ll see in my code below, I used some print statements to try to see where I went wrong, and sure enough “last_two” and “potential_plays” are returning two sets of values each instead of one. I can’t figure out why.

Sidenote: I’m aware that even after solving this issue I’ll need to tweak the code to be able to beat all 4 bots, but this is where I’m stuck right now.

Thanks in advance for any help!

def player(prev_opponent_play,
              "RRR": 0, "RRP": 0, "RRS": 0,
              "RPR": 0, "RPP": 0, "RPS": 0,
              "RSR": 0, "RSP": 0, "RSS": 0,
              "PRR": 0, "PRP": 0, "PRS": 0,
              "PPR": 0, "PPP": 0, "PPS": 0,
              "PSR": 0, "PSP": 0, "PSS": 0,
              "SRR": 0, "SRP": 0, "SRS": 0,
              "SPR": 0, "SPP": 0, "SPS": 0,
              "SSR": 0, "SSP": 0, "SSS": 0

    if not prev_opponent_play:
      prev_opponent_play = 'R'

    last_two = "".join(opponent_history[-2:])
    print("last two:", last_two)

    last_three = "".join(opponent_history[-3:])
    if len(last_three) == 3:
        play_order[0][last_three] += 1

    potential_plays = [
        last_two + "R",
        last_two + "P",
        last_two + "S",
    sub_order = {
        k: play_order[0][k]
        for k in potential_plays if k in play_order[0]

    prediction = max(sub_order, key=sub_order.get)[-1:]

    ideal_response = {'P': 'S', 'R': 'P', 'S': 'R'}
    return ideal_response[prediction]

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