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()