print("Env has ", action_size, " actions.")
measurement_size = 3 # [Battery, posion, food]
timesteps = [1, 2, 4, 8, 16, 32] # For long horizon: [4,8,16,32,64,128]
goal_size = measurement_size * len(timesteps)
img_rows, img_cols = 84, 84 #KOE: Think this is still correct.
# Convert image into Black and white
#KOETODO Not quite sure what happens here - I'm making images black/white, so what is the point?
img_channels = 3 # KOE: If I want to change this, I have to also edit the frame stacking when forming s_t
state_size = (img_rows, img_cols, img_channels)
agent = DFPAgent(state_size, measurement_size, action_size, timesteps)
agent.model = Networks.dfp_network(state_size, measurement_size, goal_size,
action_size, len(timesteps),
agent.learning_rate)
#x_t = game_state.screen_buffer # 480 x 640
#x_t = preprocessImg(initial_observation, size=(img_rows, img_cols))
#np.save("input_output_examples/initial_obs.npy", initial_observation)
#np.save("input_output_examples/preprocessed_obs.npy", x_t)
#KOE: Preprocessing to get black and white.
#KOE: Not sure what is going on here. 4 images in a row?
#s_t = np.stack(([x_t]*4), axis=2) # It becomes 64x64x4
s_t = initial_observation
s_t = np.expand_dims(s_t, axis=0) # 1x64x64x4
#np.save("input_output_examples/stacked_obs.npy", s_t)
