run_mcts (https://arxiv.org/src/1911.08265v1/anc/pseudocode.py)

pseudocode.py

# Core Monte Carlo Tree Search algorithm.
# To decide on an action, we run N simulations, always starting at the root of
# the search tree and traversing the tree according to the UCB formula until we
# reach a leaf node.
def run_mcts(config: MuZeroConfig, root: Node, action_history: ActionHistory,
             network: Network):
  min_max_stats = MinMaxStats(config.known_bounds)

  for _ in range(config.num_simulations):
    history = action_history.clone()
    node = root
    search_path = [node]

    while node.expanded():
      action, node = select_child(config, node, min_max_stats)
      history.add_action(action)
      search_path.append(node)

    # Inside the search tree we use the dynamics function to obtain the next
    # hidden state given an action and the previous hidden state.
    parent = search_path[-2]
    network_output = network.recurrent_inference(parent.hidden_state,
                                                 history.last_action())
    expand_node(node, history.to_play(), history.action_space(), network_output)

    backpropagate(search_path, network_output.value, history.to_play(),
                  config.discount, min_max_stats)