def __init__(self, env, mode = "grey", frame_count = 1, frame_skip = 1, size = 64, crop = None, record_freq = 20, torch=True):
self.name = "Wrapper"+env.name
self.env = env
self.size = size
self.grey = mode=="grey"
self.frame_count = frame_count
self.frame_skip = frame_skip
self.crop = {s:None for s in ["up","down","left","right"]}
if crop:
for s,val in zip(["up","down","left","right"],CROPS[crop]):
self.crop[s]=val
if self.grey:
self.channels = self.frame_count
else:
self.channels = 3 * self.frame_count
self.record_freq = record_freq
self.current_episode = 0
self.memory = deque([],self.frame_count)
self.recording = False
self.episode = []
self.action_space = env.action_space
if torch:
self.observation_space = Continuous((self.channels,self.size,self.size))
self.axis =(2,0,1)
else:
self.observation_space = Continuous((self.size,self.size,self.channels))
self.axis=(0,1,2)
def __init__(self, env, net, coef=1.0):
self.name = "Net" + env.name
self.env = env
self.net = net
self.action_space = env.action_space
self.observation_space = Continuous(self.net.network.output_shape)
self.coef = coef
def __init__(self, grid_size=16, max_time=5000, square_size=2):
self.square = square_size
self.grid_size = grid_size
self.max_time = max_time
self.board = np.zeros((self.grid_size, self.grid_size))
self.wall = np.zeros((self.grid_size, self.grid_size))
self.state = np.zeros(
(self.grid_size * self.square, self.grid_size * self.square, 3),
dtype=np.int32)
self.action_space = Discrete(4)
self.observation_space = Continuous(
(self.grid_size * self.square, self.grid_size * self.square, 3))
self.wall[0, :] = self.wall[:, -1] = 1
step = self.grid_size // 4
self.wall[2 * step, :] = 1
self.wall[2 * step, step:step + 2] = 0
self.wall[2 * step, 3 * step:3 * step + 2] = 0
self.wall = np.maximum(self.wall, self.wall.T)
def __init__(self,
grid_size=36,
max_time=2000,
stochastic=True,
square_size=2):
self.max_time = max_time
self.grid_size = grid_size
self.stochastic = stochastic
self.name = self.name + "Stochastic" * self.stochastic
self.square = square_size
self.action_space = Discrete(4)
self.observation_space = Continuous(
(self.grid_size * self.square, self.grid_size * self.square, 3))
self.board = np.zeros((self.grid_size, self.grid_size))
self.wall = np.zeros((self.grid_size, self.grid_size))
self.state = np.zeros(
(self.grid_size * self.square, self.grid_size * self.square, 3),
dtype=np.int32)
self.found = False
def __init__(self, *args, **kwargs):
super(GRID2, self).__init__(*args, **kwargs)
self.observation_space = Continuous((2, 2, 1))