def __init__(self, background, player, enemy):
Environment.__init__(self, background)
self.player = player
self.enemy = enemy
if isinstance(player, BattleGroup):
self.players = sprite.Group(player.groupmembers)
else:
self.players = sprite.Group(player)
if isinstance(enemy, BattleGroup):
self.enemies = sprite.Group(enemy.groupmembers)
else:
self.enemies = sprite.Group(enemy)
self.sprites = sprite.RenderUpdates(self.players, self.enemies)
self.combatants = sprite.Group(self.players, self.enemies)
self.alignCombatants(self.players, 608 - 16, Direction.LEFT)
self.alignCombatants(self.enemies, 32 + 16, Direction.RIGHT)
# TODO: Battlefield status needs to be updated
self.statusBar = BattlefieldStatus(self.players.sprites()[0], self.enemies.sprites()[0])
self.frameCount = settings.fps / 2
self.battleQueue = [(c.speed, c) for c in self.combatants]
self.battleQueue.sort(key=itemgetter(0))
def __init__(self, env_name): Environment.__init__(self) self.conn, child_conn = Pipe() self.proc = Process(target=worker, args=(child_conn, env_name)) self.proc.start() self.conn.recv() self.reset()
def __init__(self, background, player, location, walls=()):
Environment.__init__(self, background)
self.player = player
self.playergroup = sprite.GroupSingle(player)
self.walls = sprite.RenderPlain(walls)
self.npcgroup = sprite.Group()
self.sprites = sprite.RenderUpdates()
self.sprites.add(self.player)
self.statusBar = OverWorldStatus(self.player, location)
def __init__(self):
#read name file to load all the names and their types
enemies = []
self._file = os.getcwd() + "\\frozen_enemy.txt"
enemies = self.read_enemy_file(self._file)
Environment.__init__(self, "Frozen Mountains of Absolute Doom",
enemies, 0.75, randint(1, 2))
self.boss = Warrior("The Great Ice Giant, Halafor", False)
self.boss.hero_stats.hit_points += 250
self.boss.hero_stats.mana_points += 150
self.boss.hero_stats.attack_power += 70
self.boss.hero_stats.defense += 70
def __init__(self, lake, slip, max_steps, seed=None):
"""
lake: A matrix that represents the lake. For example:
lake = [['&', '.', '.', '.'],
['.', '#', '.', '#'],
['.', '.', '.', '#'],
['#', '.', '.', '$']]
slip: The probability that the agent will slip
max_steps: The maximum number of time steps in an episode
seed: A seed to control the random number generator (optional)
"""
# start (&), frozen (.), hole (#), goal ($)
# self.lake = np.zeros(np.array(lake).shape)
self.lake = np.array(lake)
self.lake_flat = self.lake.reshape(-1)
self.slip = slip
n_states = self.lake.size + 1
n_actions = 4
pi = np.zeros(n_states, dtype=float)
pi[np.where(self.lake_flat == '&')[0]] = 1.0
self.absorbing_state = n_states - 1
# TODO:
Environment.__init__(self, n_states, 4, max_steps, pi, seed)
# Up, left, down, right.
self.actions = [(-1, 0), (0, -1), (1, 0), (0, 1)]
self.itos = list(
product(range(self.lake.shape[0]), range(self.lake.shape[1])))
self.stoi = {s: i for (i, s) in enumerate(self.itos)}
self._p = np.zeros((n_states, n_states, 4))
for state_index, state in enumerate(self.itos):
for action_index, action in enumerate(self.actions):
next_state = (state[0] + action[0], state[1] + action[1])
if (state_index == 5 or state_index == 7 or state_index == 12
or state_index == 11 or state_index == 15):
self._p[state_index, state_index, action_index] = 1.0
else:
next_state_index = self.stoi.get(next_state, state_index)
self._p[next_state_index, state_index,
action_index] = 1 - self.slip
for act in self.actions:
next_state_action = (state[0] + act[0],
state[1] + act[1])
next_state_index = self.stoi.get(
next_state_action, state_index)
self._p[next_state_index, state_index,
action_index] += self.slip / 4
def __init__(self, env_name):
Environment.__init__(self)
self.right_decision = None
#self.env_instance = gym.make(env_name)
#self.last_state=self.env_instance.reset()
self.conn, child_conn = Pipe(
) ##create a 2way pipe with self.conn=parent and child_conn=child
self.proc = Process(target=worker_without_instance,
args=(child_conn, env_name))
#self.proc = Process(target=worker_with_instance, args=(child_conn, env_name,self.env_instance))
#self.last_action = 0
#self.last_reward = 0
self.proc.start()
self.conn.recv()
self.reset()
# this network will not be trained
target_net.eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
agent = Agent(policy_net.to(device), target_net.to(device))
# initialize initial state after an episode has started
state = torch.zeros(INPUT_SIZE)
# create a namedtuple for easy storage of replay memory and create 10000
# storage spaces for experiences
replay = ReplayMemory(10000)
Experience = namedtuple('Experience',
('State', 'Action', 'Reward', 'Next_State', 'Done'))
rewards_per_episode = []
rewards_episode = 0
for episode in range(EPISODES):
env.__init__()
alive = True
print('Episode: {}, Rewards Last Episode: {}'.format(
episode, rewards_episode))
rewards_episode = 0
while alive:
# choose an action and perform it
# action = 0
action = agent.choose_action(state)
done, next_state, reward = env.step(action)
# however, for the 4 frames thereafter we don't do anything and
# observe our action. This gives more valuable input to the
# network as to what the action leads to in the game. We
# concatenate this all into one state.
for frame in range(4):
def __init__(self):
Environment.__init__(self)
self.loginfo = Environment()
self.testcase = ''
def __init__(self):
Environment.__init__(self)
