def take_turn(self, game_state: GameState) -> Tuple[GameState, int]:
# First, we need to calculate the outcome of the next game states.
possible_next_states, _ = game_state.get_possible_moves()
next_scores = {
self.feed_forward(state.to_1P_array()): state
for state in possible_next_states
}
best_score = max(next_scores)
next_score = best_score if (game_state.has_player_won() is None
) else game_state.has_player_won()
next_state = next_scores.get(best_score).copy()
return next_state, next_score
# Parameters
D = deque() # Register where the actions will be stored
observetime = 500 # Number of timesteps we will be acting on the game and observing results
epsilon = 0.7 # Probability of doing a random move
gamma = 0.9 # Discounted future reward. How much we care about steps further in time
mb_size = 500 # Learning minibatch size
# FIRST STEP: Knowing what each action does (Observing)
observation = GameState(["Michael"]) # Game begins
state = observation.to_array()
done = False
turns = 0
for t in range(observetime):
possible_moves = observation.get_possible_moves()[0]
if (DEBUG):
if t < 10 or t % 20 == 0:
print("t = " + str(t))
print(observation)
print("len = " + str(len(possible_moves)))
if (t % 100 == 0 and turns >= 100):
observation = GameState(["Michael"]) # Game begins
state = observation.to_array()
done = False
turns = 0
print("RESTART: t = " + str(t))
possible_states = np.array(
list(map(lambda move: move.to_array(), possible_moves)))