| import os | |
| import argparse | |
| import pickle | |
| import keras | |
| import numpy as np | |
| from keras.layers import Dense, Multiply, Input, Conv2D, Flatten | |
| from keras.models import Sequential, Model | |
| from keras.optimizers import Adam, RMSprop, SGD | |
| from skimage.transform import resize | |
| from skimage.color import rgb2gray | |
| STORING_PATH = './results/' | |
| MODELS_PATH = './trained_models/' | |
| def save_results(environment, approximator, seed, rewards): | |
| storing_path = os.path.join(STORING_PATH, environment, approximator, str(seed)) | |
| if not os.path.exists(storing_path): | |
| os.makedirs(storing_path) | |
| np.save(storing_path + '/' + 'upside_down_rewards.npy', rewards) | |
| def get_functional_behaviour_function(state_size, command_size, action_size): | |
| observation_input = keras.Input(shape=(state_size,)) | |
| linear_layer = Dense(64, activation='sigmoid')(observation_input) | |
| command_input = keras.Input(shape=(command_size,)) | |
| sigmoidal_layer = Dense(64, activation='sigmoid')(command_input) | |
| multiplied_layer = Multiply()([linear_layer, sigmoidal_layer]) | |
| layer_1 = Dense(64, activation='relu')(multiplied_layer) | |
| layer_2 = Dense(64, activation='relu')(layer_1) | |
| layer_3 = Dense(64, activation='relu')(layer_2) | |
| layer_4 = Dense(64, activation='relu')(layer_3) | |
| final_layer = Dense(action_size, activation='softmax')(layer_4) | |
| model = Model(inputs=[observation_input, command_input], outputs=final_layer) | |
| model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001)) | |
| return model | |
| def get_atari_behaviour_function(action_size): | |
| print('Getting the model') | |
| input_state = Input(shape=(84,84,4)) | |
| first_conv = Conv2D( | |
| 32, (8, 8), strides=(4,4), activation='relu')(input_state) | |
| second_conv = Conv2D( | |
| 64, (4, 4), strides=(2,2), activation='relu')(first_conv) | |
| third_conv = Conv2D( | |
| 64, (3, 3), strides=(1,1), activation='relu')(second_conv) | |
| flattened = Flatten()(third_conv) | |
| dense_layer = Dense(512, activation='relu')(flattened) | |
| command_input = keras.Input(shape=(2,)) | |
| sigmoidal_layer = Dense(512, activation='sigmoid')(command_input) | |
| multiplied_layer = Multiply()([dense_layer, sigmoidal_layer]) | |
| final_layer = Dense(256, activation='relu')(multiplied_layer) | |
| action_layer = Dense(action_size, activation='softmax')(final_layer) | |
| model = Model(inputs=[input_state, command_input], outputs=action_layer) | |
| model.compile(loss='categorical_crossentropy', optimizer=RMSprop(lr=0.001, rho=0.95, epsilon=0.01)) | |
| print(model.summary()) | |
| return model | |
| def get_catch_behaviour_function(action_size): | |
| print('Getting the Catch-model') | |
| input_state = Input(shape=(84,84,4)) | |
| first_conv = Conv2D( | |
| 32, (8, 8), strides=(4,4), activation='relu')(input_state) | |
| second_conv = Conv2D( | |
| 64, (4, 4), strides=(2,2), activation='relu')(first_conv) | |
| third_conv = Conv2D( | |
| 64, (3, 3), strides=(1,1), activation='relu')(second_conv) | |
| flattened = Flatten()(third_conv) | |
| dense_layer = Dense(512, activation='relu')(flattened) | |
| command_input = keras.Input(shape=(2,)) | |
| sigmoidal_layer = Dense(512, activation='sigmoid')(command_input) | |
| multiplied_layer = Multiply()([dense_layer, sigmoidal_layer]) | |
| final_layer = Dense(256, activation='relu')(multiplied_layer) | |
| action_layer = Dense(action_size, activation='softmax')(final_layer) | |
| model = Model(inputs=[input_state, command_input], outputs=action_layer) | |
| model.compile(loss='categorical_crossentropy', optimizer=RMSprop(lr=0.001, rho=0.95, epsilon=0.01)) | |
| print(model.summary()) | |
| return model | |
| def pre_processing(state): | |
| processed_state = np.uint8( | |
| resize(rgb2gray(state), (84, 84), mode='constant')*255) | |
| return processed_state | |
| def save_trained_model(environment, seed, model): | |
| storing_path = os.path.join(MODELS_PATH, environment, str(seed)) | |
| if not os.path.exists(storing_path): | |
| os.makedirs(storing_path) | |
| model.save_weights(storing_path + '/' + 'trained_model.h5') | |