q_func = np.zeros((NUM_ROOM_DESC, NUM_QUESTS, NUM_ACTIONS, NUM_OBJECTS))
single_run_epoch_rewards_test = []
pbar = tqdm(range(NUM_EPOCHS), ncols=80)
for _ in pbar:
single_run_epoch_rewards_test.append(run_epoch())
pbar.set_description(
"Avg reward: {:0.6f} | Ewma reward: {:0.6f}".format(
np.mean(single_run_epoch_rewards_test),
utils.ewma(single_run_epoch_rewards_test)))
return single_run_epoch_rewards_test
if __name__ == '__main__':
# Data loading and build the dictionaries that use unique index for each state
(dict_room_desc, dict_quest_desc) = framework.make_all_states_index()
NUM_ROOM_DESC = len(dict_room_desc)
NUM_QUESTS = len(dict_quest_desc)
# set up the game
framework.load_game_data()
epoch_rewards_test = [] # shape NUM_RUNS * NUM_EPOCHS
for _ in range(NUM_RUNS):
epoch_rewards_test.append(run())
epoch_rewards_test = np.array(epoch_rewards_test)
x = np.arange(NUM_EPOCHS)
fig, axis = plt.subplots()
def run_episode(for_training):
""" Runs one episode
If for training, update Q function
If for testing, computes and return cumulative discounted reward
Args:
for_training (bool): True if for training
Returns:
None
"""
epsilon = TRAINING_EP if for_training else TESTING_EP
dict_room_desc, dict_quest_desc = framework.make_all_states_index()
# q_func = np.zeros((NUM_ROOM_DESC, NUM_QUESTS, NUM_ACTIONS, NUM_OBJECTS))
epi_reward = 0
# initialize for each episode
# TODO Your code here
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
t = 0
while not terminal:
# Choose next action and execute
# TODO Your code here
current_room = dict_room_desc[
current_room_desc] # Índice de habitación
current_quest = dict_quest_desc[
current_quest_desc] # Índice de la quest
# Decidir acción con épsilon greedy
action_index, object_index = epsilon_greedy(current_room,
current_quest, q_func,
epsilon)
# Paso del juego + traducir descripciones
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(
current_room_desc, current_quest_desc, action_index, object_index)
next_room = dict_room_desc[next_room_desc]
next_quest = dict_quest_desc[next_quest_desc]
if for_training:
# update Q-function.
# TODO Your code here
tabular_q_learning(q_func, current_room, current_quest,
action_index, object_index, reward, next_room,
next_quest, terminal)
pass
if not for_training:
# update reward
# TODO Your code here
epi_reward += (GAMMA**t) * reward
pass
# prepare next step
# TODO Your code here
t += 1
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
if not for_training:
return epi_reward
