if LOAD_MODEL:
print("Loading model from: ", model_savefile)
saver.restore(SESSION, model_savefile)
else:
init = tf.global_variables_initializer()
SESSION.run(init)
##########################################
if not SKIP_LEARNING:
time_start = time()
print("\nFilling out replay memory")
updateTarget(targetOps, SESSION)
agent.reset_cell_state()
state = game.get_state()
for _ in range(RANDOM_WANDER_STEPS):
if not LOAD_MODEL:
action = agent.random_action()
else:
action = agent.act(game.get_last_action(), state)
img_state, reward, done = game.make_action(action)
if not done:
state_new = img_state
else:
state_new = None
agent.add_transition(state, action, reward, state_new, done)
state = state_new
if done:
print("Loading model from: ", model_savefile)
saver.restore(SESSION, model_savefile)
else:
init = tf.global_variables_initializer()
SESSION.run(init)
##########################################
if not SKIP_LEARNING:
time_start = time()
print("\nFilling out replay memory")
updateTarget(targetOps, SESSION)
episode_buffer = []
agent.reset_cell_state()
state = preprocess(game.get_state())
for _ in trange(RANDOM_WANDER_STEPS, leave=False):
action = agent.random_action()
s, reward, d = game.make_action(action)
done = game.is_terminared()
if not done:
state_new = preprocess(game.get_state())
else:
state_new = None
agent.add_transition(state, action, reward, state_new, done)
state = state_new
if done:
game.reset()
agent.reset_cell_state()
