Working MCTS implementation

This is a basic working implementation of the MCTS algorithm. Though
currently the algorithm is slow compared with other implemenations, and
makes sub-optimal choices when playing tic-tac-toe. Therefore some
modifications are needed

src/policy/simulation/random.rs

@@ -0,0 +1,18 @@
+//! Random play simulation policy
+//!
+//! Actions are chosen at random
+
+use crate::state::GameState;
+use crate::tree::node::{Node, RewardVal};
+use rand::prelude::SliceRandom;
+use std::collections::HashMap;
+
+pub fn simulate<S: GameState>(node: &Node<S>) -> HashMap<S::Player, RewardVal> {
+    let mut state: S = node.state.clone();
+    while !state.is_terminal() {
+        let legal_actions = state.get_legal_actions();
+        let action = legal_actions.choose(&mut rand::thread_rng()).unwrap();
+        state = state.state_after_action(&action);
+    }
+    state.rewards_for_players()
+}