def __init__(self):
"""
configure
@param memory: store tuning information
@param enviroment: implement enviroment----filter
@param networks: AI
"""
# configure the enviroment
self.myfilter = Filter()
# configure the networks
self.net_models = Nnet()
# configure the memory
self.data_memory = MemoryD(self.memory_size, self.myfilter.state_size,
self.state_nbr)
"""
random demostration for calculate the avg qvaluse
"""
random_screws = np.zeros((self.random_nr, 2), dtype=np.float64)
for i in range(self.random_nr):
random_screws[i] = ([
random.uniform(self.myfilter.screw_min,
self.myfilter.screw_max),
random.uniform(self.myfilter.screw_min,
self.myfilter.screw_max)
])
# Fetch random states
random_states = self.myfilter.new_tuning(random_screws)
# dimensionality reduction
self.dr_random_states = self.myfilter.dimreduction_pca(
random_states.transpose())
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, game_name, run_id):
self.number_of_actions = len(action_dict[game_name])
valid_actions = action_dict[game_name]
net.layers[-2] = dp.FullyConnected(n_output=self.number_of_actions,
weights=dp.Parameter(
dp.NormalFiller(sigma=0.1),
weight_decay=0.004,
monitor=False))
self.memory = MemoryD(self.memory_size)
self.ale = ALE(valid_actions,
run_id,
display_screen="false",
skip_frames=4,
game_ROM='ale/roms/' + game_name + '.bin')
self.nnet = net
self.q_values = []
self.test_game_scores = []
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")
def setUp(self):
self.memory = MemoryD(10)
