def play(g: Game, ann: ANN):
# Initial 2 players
player1 = g.get_players(1)[0]
player2 = g.get_players(2)[0]
player3 = g.get_players(2)[1]
player4 = g.get_players(2)[2]
player1.main_player = True
# Start the game (flag)
g.start()
g.set_train(True)
g.update()
g.update_state()
g.prev_stat = g.get_state_stat()
# print(g.get_resource_matrix())
while True:
# If the game is in terminal state
if g.is_game_terminal() or g.get_ticks() > 3000:
file = open("./logs/evaluation.txt", 'a+')
g.game_result(file)
file.close()
g.stop()
print("Game over")
break
# print("Player 1 : " + str(player1.location))
# Player 1 action by model
# TODO 599: Use the predicted action
state = g.get_state()
predicted_action = ann.predict_action(state)
random_action = np.random.randint(2)
if (np.random.random() < max(0.01, np.power(ann.epsilon, ann.tick))):
action = random_action
else:
action = predicted_action
#print("Player1 Action:", action)
player1.do_action(action)
# # # Player 2 random action
player2.do_action(2)
player3.do_action(2)
player4.do_action(2)
update_with_skip_rate(g, SKIP_RATE)
g.render() # Draw the game state to graphics
g.caption() # Show Window caption
g.update_state() # Update states to new model
# print("Resource at ", i)
# # print(g.get_resource_matrix())
# print("Player Resource:", player1.player.getScore())
g.view() # View the game state in the pygame window
next_state = g.get_state()
reward = g.get_reward_value()
ann.train(state, action, reward, next_state, g.is_terminal())
state = next_state
def play(g: Game, ac: Actor_Critic, game_num):
# Initial 2 players
player1 = g.get_players(1)[0]
player2 = g.get_players(2)[0]
player3 = g.get_players(2)[1]
#player4 = g.get_players(2)[2]
player1.main_player = True
# Start the game (flag)
g.start()
g.set_train(True)
g.update()
g.update_state()
player1.build_town_hall()
player3.build_town_hall()
update_with_skip_rate(g, 10)
g.update_state()
g.prev_stat = g.get_state_stat()
# print(g.get_resource_matrix())
state = g.get_state()
while True:
# If the game is in terminal state
if not TRAIN:
time.sleep(0.5)
if g.is_game_terminal():
if TRAIN:
file = open("./logs_ac/evaluation_ac.csv", 'a+')
g.game_result(file, game_num)
file.close()
else:
g.game_result(None, game_num)
g.stop()
print("Game over")
break
# Player 1 action by model
action = ac.predict_action(state)
#print("Player1 Action:", action)
player1.do_action(action)
# # # Player 2 random action
random_action = np.random.randint(4)
player2.do_action(random_action)
player3.do_action(4)
update_with_skip_rate(g, SKIP_RATE)
g.update_state() # Update states to new model
g.view() # View the game state in the pygame window
reward = g.get_reward_value()
next_state = g.get_state()
if (TRAIN):
ac.learn(state, action, reward, next_state, g.is_game_terminal())
state = next_state
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]
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_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(np.random.randint(1, 4))
update_with_skip_rate(g, SKIP_RATE)
g.caption() # Show Window caption
g.update_state() # Update states to new model
g.view() # View the game state in the pygame window
next_state = g.capture_grey_scale()
reward = g.get_reward_value()
# ddqn.train(state, action, reward, next_state, g.is_terminal())
state = next_state
def play(g: Game, ann: ANN, game_num):
# Initial 2 players
player1 = g.get_players(1)[0]
player2 = g.get_players(2)[0]
player3 = g.get_players(2)[1]
#player4 = g.get_players(2)[2]
player1.main_player = True
# Start the game (flag)
g.start()
g.set_train(True)
g.update()
g.update_state()
player1.build_town_hall()
player3.build_town_hall()
update_with_skip_rate(g, 10)
g.update_state()
g.prev_stat = g.get_state_stat()
# print(g.get_resource_matrix())
state = g.get_state()
i = 0
while True:
# If the game is in terminal state
if not TRAIN:
time.sleep(0.3)
if g.is_game_terminal():
if TRAIN:
ex = "PER" if ann.PER else "ER"
file = open("./logs_ann/evaluation_" + ex + ".csv", 'a+')
g.game_result(file, game_num)
file.close()
else:
g.game_result(None, game_num)
g.stop()
print("Game over")
break
# print("Player 1 : " + str(player1.location))
# Player 1 action by model
# TODO 599: Use the predicted action
predicted_action = ann.predict_action(state)
random_action = np.random.randint(4)
if (np.random.random() < max(0.01, np.power(ann.epsilon, ann.tick))):
action = random_action
else:
action = predicted_action
#print("Player1 Action:", action)
player1.do_action(action)
# # # # Player 2 random action
player2.do_action(random_action)
player3.do_action(4)
#player4.do_action(2)
update_with_skip_rate(g, SKIP_RATE)
g.update_state() # Update states to new model
g.view() # View the game state in the pygame window
next_state = g.get_state()
reward = g.get_reward_value()
ann.train(state, action, reward, next_state, g.is_game_terminal())
state = next_state
