Exemple #1
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 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",
                           discount_factor=self.discount_factor)
Exemple #2
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 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')
Exemple #3
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    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')