def __init__(self):
self.memory = MemoryD(self.memory_size)
self.ale = ALE(display_screen="true",
skip_frames=4,
game_ROM='../libraries/ale/roms/breakout.bin')
self.nnet = NeuralNet(self.state_size, self.number_of_actions,
"ai/deepmind-layers.cfg",
"ai/deepmind-params.cfg", "layer4")
def __init__(self):
self.memory = MemoryD(self.memory_size)
self.minibatch_size = 32 # Given in the paper
self.number_of_actions = 4 # Game "Breakout" has 4 possible actions
# Properties of the neural net which come from the paper
self.nnet = NeuralNet([1, 4, 84, 84],
filter_shapes=[[16, 4, 8, 8], [32, 16, 4, 4]],
strides=[4, 2],
n_hidden=256,
n_out=self.number_of_actions)
self.ale = ALE(self.memory)
def __init__(self):
self.minibatch_size = 32 # Given in the paper
self.number_of_actions = 4 # XXX Game "Breakout" has 4 possible actions
# Properties of the neural net which come from the paper
self.nnet = NeuralNet([1, 4, 84, 84],
filter_shapes=[[16, 4, 8, 8], [32, 16, 4, 4]],
strides=[4, 2],
n_hidden=256,
n_out=self.number_of_actions)
self.ale = ALE()
self.frames_played = 0
self.iterations_per_choice = 4
self.games = games.Games()
def __init__(self):
#self.memory = MemoryD(self.memory_size)
self.memory = DataSet(80, 80, self.memory_size, 4)
self.ale = ALE(display_screen="true",
skip_frames=4,
game_ROM='../libraries/ale/roms/breakout.bin')
#self.nnet = NeuralNet(self.state_size, self.number_of_actions, "ai/deepmind-layers.cfg", "ai/deepmind-params.cfg", "layer4")
self.nnet = CNNQLearner(self.number_of_actions,
4,
80,
80,
discount=.9,
learning_rate=.0001,
batch_size=32,
approximator='none')
def __init__(self):
# initialize memory
#self.memory = MemoryD(self.memory_size)
self.memory = DataSet(self.image_size, self.image_size,
self.memory_size, 4)
# initalize ALE
self.ale = ALE(display_screen="true",
skip_frames=4,
game_ROM='../libraries/ale/roms/breakout.bin',
preprocess_type=self.preprocess_type)
# initialize neural network
#self.nnet = NeuralNet(self.state_size, self.number_of_actions, "ai/deepmind-layers.cfg",
# "ai/deepmind-params.cfg", "layer4", discount_factor= self.discount_factor)
self.nnet = CNNQLearner(self.number_of_actions,
4,
self.image_size,
self.image_size,
discount=self.discount_factor,
learning_rate=.0001,
batch_size=32,
approximator='cuda_conv')
def __init__(self):
self.memory = MemoryD(self.memory_size)
self.ale = ALE(self.memory)
self.nnet = NeuralNet(self.state_size, self.number_of_actions,
"ai/deepmind-layers.cfg",
"ai/deepmind-params.cfg", "layer4")
