Run a Monte Carlo Tree Search (MCTS) Simulation

MCTS.py

# https://github.com/suragnair/alpha-zero-general/blob/5156c7fd1d2f3e5fefe732a4b2e0ffc5b272f819/MCTS.py#L37-L48

for i in range(self.args.numMCTSSims):  # self.args.numMCTSSims, the number of MCTS simulations to compute
    self.search(canonicalBoard)  # "search" is a MCTS simulations

s = self.game.stringRepresentation(canonicalBoard)
# Count how many times we have visited each node
counts = [self.Nsa[(s, a)] if (s, a) in self.Nsa else 0 for a in range(self.game.getActionSize())]

if temp == 0:
    # Pick the node that was visited the most
    bestAs = np.array(np.argwhere(counts == np.max(counts))).flatten()
    bestA = np.random.choice(bestAs)
    probs = [0] * len(counts)
    probs[bestA] = 1
    return probs