def main():
map_generator()
game = Game(MAP_NAME)
game.add_a_player(2)
game.add_a_player(2)
for _ in range(NUM_OF_GAMES):
play(game)
game.reset()
def events_hack():
# Hack to render the pygame
events = pygame.event.get()
def get_random_action():
return numpy.random.randint(0, 16)
if __name__ == "__main__":
if TRAIN:
os.environ["SDL_VIDEODRIVER"] = "dummy"
SKIP_RATE = 2
file = open("./logs/evaluation.txt", 'a+')
file.write(
"team_health,team_count,team_avg_health,team_resource,o_team_health,o_team_count,o_team_health_avg,o_team_resource,result,time_ticks"
)
file.close()
game = Game(MAP_NAME, train=TRAIN)
game.add_a_player(2)
game.add_a_player(2)
ann = ANN()
print(ann.get_summary())
for _ in range(11):
play(game, ann)
game.reset()
if (_ % 10 == 0):
ann.save_model(str(int(_ / 10)))
def play(g: Game, ddqn: DoubleDeepQNetwork, NPC_Memory: NPC_History, use_NPC=False):
# Initial 2 players
player1 = g.get_players(1)[0]
player2 = g.get_players(2)[0]
player2_1 = g.add_a_player(2)
player2_2 = g.add_a_player(2)
player1.main_player = True
# Setup action list
global action_list
if use_NPC:
action_list = collections.deque(maxlen=5)
start_game(g)
state = g.capture_grey_scale()
g.prev_stat = g.get_state_stat()
while True:
if g.is_game_terminal():
g.stop()
if use_NPC:
action_list.clear()
g.game_result()
print("Game over")
break
# Spots
print("Player 1 : " + str(player1.location) + " - " + str(player1.health_p) + " - " + str(player1.gold))
print("Player 2 : " + str(player2.location) + " - " + str(player2.health_p) + " - " + str(player2.gold))
# Player 1 action by model
# action = ddqn.predict_action(state)
action = 1
player1.do_action(ATTACK_CLOSEST_TARGET)
# Only for NPC as current model is no good
if use_NPC is True:
npc_action = np.random.randint(2)
if len(action_list) >= 5:
# TODO dummy_get_npc_state
player_health = player1.health_p
resource_matrix = g.get_resource_matrix()
# print(resource_matrix.shape)
x, y = g.get_closest_enemy_location(player1.location[0], player1.location[1], 1)
dist_closest_enemy = (np.abs(player1.location[0] - x) + np.abs(player2.location[1] - y))
reward_value = g.get_reward_value()
number_of_enemies = len(g.teams[Game.OPPONENTS[1]].players)
NPC_state = [action_list[0], action_list[1], action_list[2], action_list[3], action_list[4],
player_health, np.sum(np.sum(resource_matrix, axis=0), axis=0), dist_closest_enemy,
reward_value, number_of_enemies]
# print(NPC_state)
# NPC_Memory.Add_Observation(NPC_state,action)
NPC_Memory.Add_Observation(NPC_state, npc_action)
print(NPC_Memory.num_obs)
if NPC_Memory.num_obs > 100 and NPC_Memory.do_once_flag is True:
print("GOT HERE __________----------")
NPC_History.do_once_flag = False
player_net = NPC_CatBoost('Follower')
player_net.train_(NPC_Memory.CAT_State, NPC_Memory.CAT_Action)
player_net.eval_train()
if use_NPC is True:
action_list.append(action)
# Player 2 random action
player2.do_action(get_random_action())
player2_1.do_action(RANDOM_MOVE)
player2_2.do_action(RANDOM_MOVE)
update_with_skip_rate(g, SKIP_RATE)
g.update_state() # Update states to new model
next_state = g.capture_grey_scale()
reward = g.get_reward_value()
# ddqn.train(state, action, reward, next_state, g.is_terminal())
state = next_state
