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
epi_reward = None
# initialize for each episode
# TODO Your code here
epi_reward = 0
t = 0
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
# TODO Your code here
current_state = current_room_desc + current_quest_desc
current_state_vector = utils.extract_bow_feature_vector(
current_state, dictionary)
if for_training:
# update Q-function.
# TODO Your code here
action_index, object_index = epsilon_greedy(current_state_vector, theta, epsilon)
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(
current_room_desc, current_quest_desc,
action_index, object_index)
next_state = next_room_desc + next_quest_desc
next_state_vector = utils.extract_bow_feature_vector(
next_state, dictionary)
linear_q_learning(theta, current_state_vector, action_index, object_index,
reward, next_state_vector, terminal)
if not for_training:
# update reward
# TODO Your code here
action_index, object_index = epsilon_greedy(current_state_vector, theta, epsilon)
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(
current_room_desc, current_quest_desc,
action_index, object_index)
epi_reward = epi_reward + reward * (GAMMA**t)
t = t + 1
# prepare next step
# TODO Your code here
# update current_room_desc and current_quest_desc
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
if not for_training:
return epi_reward
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
# initialize for each episode
# TODO Your code here
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
# initial value
count = 0
epi_reward = 0
while not terminal:
# Choose next action and execute
# TODO Your code here
# recall index from dictionary by "description" key
current_state_1 = dict_room_desc[current_room_desc]
current_state_2 = dict_quest_desc[current_quest_desc]
(action_index, object_index) = epsilon_greedy(current_state_1,
current_state_2, q_func,
epsilon)
(next_room_desc, next_quest_desc, reward, terminal) \
= framework.step_game(current_room_desc, current_quest_desc, action_index, object_index)
next_state_1 = dict_room_desc[next_room_desc]
next_state_2 = dict_quest_desc[next_quest_desc]
if for_training:
# update Q-function.
# TODO Your code here
tabular_q_learning(q_func, current_state_1, current_state_2,
action_index, object_index, reward,
next_state_1, next_state_2, terminal)
if not for_training:
# update reward
# TODO Your code here
epi_reward += np.power(GAMMA, count) * reward
# prepare next step
# TODO Your code here
count += 1
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
if not for_training:
return epi_reward
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
"""
if for_training:
epsilon = TRAINING_EP
else:
epsilon = TESTING_EP
epi_reward = 0
current_room_desc, current_quest_desc, terminal = framework.newGame()
step = 0
while not terminal:
state_1, state_2 = dict_room_desc[current_room_desc], dict_quest_desc[current_quest_desc]
action_index, object_index = epsilon_greedy(state_1, state_2, q_func, epsilon)
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(current_room_desc, current_quest_desc, action_index, object_index)
next_state_1, next_state_2 = dict_room_desc[next_room_desc], dict_quest_desc[next_quest_desc]
if for_training:
tabular_q_learning(q_func, state_1, state_2, action_index,object_index, reward, next_state_1, next_state_2, terminal)
if not for_training:
epi_reward+=(reward*(GAMMA)**step)
step+=1
if not for_training:
return epi_reward
def run_episode(for_training):
"""
Runs one episode
If for training, update Q function
If for testing, computes and return cumulative discounted reward
"""
epsilon = TRAINING_EP if for_training else TESTING_EP
epi_reward = None
# initialize for each episode
epi_reward = 0
gamma_t = 1
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
current_state = current_room_desc + current_quest_desc
current_state_vector = torch.FloatTensor(
utils.extract_bow_feature_vector(current_state, dictionary))
# choose the next action based on epsilon greedy policy
(action_index, object_index) = epsilon_greedy(current_state_vector,
epsilon)
# go to the next state based on the action
(next_room_desc, next_quest_desc, reward,
terminal) = framework.step_game(current_room_desc, current_quest_desc,
action_index, object_index)
# extract the feature vector of the next state
next_state = next_room_desc + next_quest_desc
next_state_vector = torch.FloatTensor(
utils.extract_bow_feature_vector(next_state, dictionary))
if for_training:
# update Q-function.
# TODO Your code here
#update the parameters of deep Q network
deep_q_learning(current_state_vector, action_index, object_index,
reward, next_state_vector, terminal)
#pass
if not for_training:
# update reward
#update episodic reward with discount
epi_reward += gamma_t * reward
#pass
# prepare next step
#update current state to the next state
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
gamma_t = gamma_t * GAMMA
if not for_training:
return epi_reward
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
global STEP_COUNT
STEP_COUNT=0
# initialize for each episode
epi_reward = 0.0
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
STEP_COUNT+=1
# Choose next action and execute
current_room_desc_index = dict_room_desc[current_room_desc]
current_quest_desc_index = dict_quest_desc[current_quest_desc] # Get room and quest indices
next_action_index, next_object_index = epsilon_greedy(current_room_desc_index,
current_quest_desc_index,
q_func,
epsilon) # Get next action/object
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(
current_room_desc,
current_quest_desc,
next_action_index,
next_object_index) # Take a step
# Only need room index; quest remains same during an episode
next_room_desc_index = dict_room_desc[next_room_desc]
if for_training:
# update Q-function.
tabular_q_learning(q_func, current_room_desc_index, current_quest_desc_index,
next_action_index, next_object_index, reward,
next_room_desc_index, current_quest_desc_index,
terminal)
if not for_training:
# update reward
epi_reward += (GAMMA**(framework.STEP_COUNT - 1))*reward
# prepare next step
current_room_desc, current_quest_desc = next_room_desc, next_quest_desc
if not for_training:
return epi_reward
def run_episode(for_training):
"""
Runs one episode
If for training, update Q function
If for testing, computes and return cumulative discounted reward
"""
epsilon = TRAINING_EP if for_training else TESTING_EP
# initialize for each episode
# TODO Your code here
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
count = 0
epi_reward = 0
while not terminal:
# Choose next action and execute
current_state = current_room_desc + current_quest_desc
current_state_vector = torch.FloatTensor(
utils.extract_bow_feature_vector(current_state, dictionary))
# TODO Your code here
(action_index, object_index) = epsilon_greedy(current_state_vector,
epsilon)
(next_room_desc, next_quest_desc, reward, terminal) \
= framework.step_game(current_room_desc, current_quest_desc, action_index, object_index)
next_state = next_room_desc + next_quest_desc
next_state_vector = torch.FloatTensor(
utils.extract_bow_feature_vector(next_state, dictionary))
if for_training:
# update Q-function.
# TODO Your code here
deep_q_learning(current_state_vector, action_index, object_index,
reward, next_state_vector, terminal)
pass
if not for_training:
# update reward
# TODO Your code here
epi_reward += np.power(GAMMA, count) * reward
pass
# prepare next step
# TODO Your code here
count += 1
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
if not for_training:
return epi_reward
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
epi_reward = 0
gamma = 1
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
current_room_index = dict_room_desc[current_room_desc]
current_quest_index = dict_quest_desc[current_quest_desc]
# Choose next action and execute
# TODO Your code here
action_index, object_index = epsilon_greedy(current_room_index,
current_quest_index,
q_func, epsilon)
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(
current_room_desc, current_quest_desc, action_index, object_index)
next_room_index = dict_room_desc[next_room_desc]
next_quest_index = dict_quest_desc[next_quest_desc]
if for_training:
# update Q-function.
# TODO Your code here
tabular_q_learning(q_func, current_room_index, current_quest_index,
action_index, object_index, reward,
next_room_index, next_quest_index, terminal)
pass
if not for_training:
# update reward
# TODO Your code here
epi_reward += reward * gamma
gamma *= GAMMA
pass
# prepare next step
# TODO Your code here
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
if not for_training:
return epi_reward
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
epi_reward = 0.00
t = 0
# initialize for each episode
# TODO Your code here
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
# TODO Your code here
state_1 = dict_room_desc[current_room_desc]
state_2 = dict_quest_desc[current_quest_desc]
(action_index, object_index) = epsilon_greedy(state_1, state_2, q_func,
epsilon)
(next_room_desc, next_quest_desc, reward,
terminal) = framework.step_game(current_room_desc, current_quest_desc,
action_index, object_index)
(next_state_1, next_state_2) = (dict_room_desc[next_room_desc],
dict_quest_desc[next_quest_desc])
if for_training:
# update Q-function.
# TODO Your code here
tabular_q_learning(q_func, state_1, state_2, action_index,
object_index, reward, next_state_1,
next_state_2, terminal)
if not for_training:
# update reward
# TODO Your code here
epi_reward = epi_reward + (GAMMA**t) * reward
t += 1
current_room_desc, current_quest_desc = next_room_desc, next_quest_desc
# prepare next step
# TODO Your code here
if not for_training:
return epi_reward
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
gamma_step = 1
epi_reward = 0
# initialize for each episode
# TODO Your code here
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
# TODO Your code here
cur_room_desc_id = dict_room_desc[current_room_desc]
cur_quest_desc_id = dict_quest_desc[current_quest_desc]
(action_index, object_index) = epsilon_greedy(cur_room_desc_id,
cur_quest_desc_id,
q_func, epsilon)
(next_room_desc, next_quest_desc, reward,
terminal) = framework.step_game(current_room_desc, current_quest_desc,
action_index, object_index)
if for_training:
# update Q-function.
# TODO Your code here
next_room_desc_id = dict_room_desc[next_room_desc]
next_quest_desc_id = dict_quest_desc[next_quest_desc]
tabular_q_learning(q_func, cur_room_desc_id, cur_quest_desc_id,
action_index, object_index, reward,
next_room_desc_id, next_quest_desc_id, terminal)
if not for_training:
# update reward
# TODO Your code here
epi_reward = epi_reward + gamma_step * reward
gamma_step = gamma_step * GAMMA
# prepare next step
# TODO Your code here
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
if not for_training:
return epi_reward
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
# initialize for each episode
epi_reward = 0.
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
current_state_1 = dict_room_desc.get(current_room_desc)
current_state_2 = dict_quest_desc.get(current_quest_desc)
steps = 0
while not terminal:
steps += 1
# Choose next action and execute
action_index, object_index = epsilon_greedy(current_state_1,
current_state_2, q_func,
epsilon)
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(
current_room_desc, current_quest_desc, action_index, object_index)
current_state_1 = dict_room_desc.get(current_room_desc)
current_state_2 = dict_quest_desc.get(current_quest_desc)
next_state_1 = dict_room_desc.get(next_room_desc)
next_state_2 = dict_quest_desc.get(next_quest_desc)
if for_training:
# update Q-function.
tabular_q_learning(q_func, current_state_1, current_state_2,
action_index, object_index, reward,
next_state_1, next_state_2, terminal)
pass
if not for_training:
# update reward
epi_reward += GAMMA**(steps - 1) * reward
pass
# prepare next step
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
if not for_training:
return epi_reward
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
epi_reward = 0
# initialize for each episode
# TODO Your code here
num_steps = 0
# dict_room_desc, dict_quest_desc = framework.make_all_states_index()
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
# TODO Your code here
state_r, state_q = dict_room_desc[current_room_desc], dict_quest_desc[
current_quest_desc]
next_action, next_object = epsilon_greedy(state_r, state_q, q_func,
epsilon)
next_room_desc, next_quest_desc, reward, terminal = (
framework.step_game(current_room_desc, current_quest_desc,
next_action, next_object))
if for_training:
# update Q-function.
# TODO Your code here
next_state_r, next_state_q = dict_room_desc[
next_room_desc], dict_quest_desc[next_quest_desc]
tabular_q_learning(q_func, state_r, state_q, next_action,
next_object, reward, next_state_r, next_state_q,
terminal)
if not for_training:
# update reward
# TODO Your code here
epi_reward = np.power(GAMMA, num_steps) * reward + epi_reward
# prepare next step
# TODO Your code here
current_room_desc, current_quest_desc = next_room_desc, next_quest_desc
num_steps += 1
if not for_training:
return epi_reward
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
epi_reward = 0
t = 0 # step
# initialize for each episode
# TODO Your code here
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
current_room_desc_index = dict_room_desc[current_room_desc]
current_quest_desc_index = dict_quest_desc[current_quest_desc]
next_action_index, next_object_index = epsilon_greedy(
current_room_desc_index, current_quest_desc_index, q_func, epsilon)
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(
current_room_desc, current_quest_desc, next_action_index,
next_object_index)
next_room_desc_index = dict_room_desc[
next_room_desc] # quest remains same
if for_training:
# update Q-function.
tabular_q_learning(q_func, current_room_desc_index,
current_quest_desc_index, next_action_index,
next_object_index, reward, next_room_desc_index,
current_quest_desc_index, terminal)
if not for_training:
# update reward
epi_reward += (GAMMA**t) * reward
# prepare next step
current_room_desc, current_quest_desc = next_room_desc, next_quest_desc
t += 1
if not for_training:
return epi_reward
def run_episode(for_training):
"""
Model, optimiser are freaking global
Also dictionary. Crap.
Runs one episode
If for training, update Q function
If for testing, computes and return cumulative discounted reward
"""
epsilon = TRAINING_EP if for_training else TESTING_EP
epi_reward = 0.
t = 0
# initialize for each episode
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
current_state = current_room_desc + current_quest_desc
current_state_vector = torch.FloatTensor(
utils.extract_bow_feature_vector(current_state, dictionary))
action_index, object_index = epsilon_greedy(
current_state_vector, epsilon
)
next_room_desc, next_quest_desc, reward, terminal= framework.step_game(
current_room_desc,
current_quest_desc,
action_index, object_index
)
next_state_vector = torch.FloatTensor(
utils.extract_bow_feature_vector(
next_room_desc + next_quest_desc, dictionary
)
)
if for_training:
# update Q-function.
deep_q_learning(
current_state_vector,
action_index, object_index,
reward,
next_state_vector,
terminal
)
else:
# if not for_training:
# update reward
epi_reward += (GAMMA**t)*reward
# prepare next step
t += 1
if not for_training:
return epi_reward
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
epi_reward = 0
# initialize for each episode
count = 0
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
curr_state1 = dict_room_desc[current_room_desc]
curr_state2 = dict_quest_desc[current_quest_desc]
while not terminal:
# Choose next action and execute
(action_index, object_index) = epsilon_greedy(curr_state1, curr_state2,
q_func, epsilon)
(next_room_desc, next_quest_desc, reward, terminal) = \
framework.step_game(current_room_desc, current_quest_desc,
action_index, object_index)
next_state1 = dict_room_desc[next_room_desc]
next_state2 = dict_quest_desc[next_quest_desc]
if for_training:
# update Q-function.
tabular_q_learning(q_func, curr_state1, curr_state2, action_index,
object_index, reward, next_state1, next_state2,
terminal)
if not for_training:
# update reward
epi_reward += (GAMMA**count) * reward
count += 1
# prepare next step
curr_state1 = next_state1
curr_state2 = next_state2
current_room_desc = list(dict_room_desc.keys())[list(
dict_room_desc.values()).index(curr_state1)]
current_quest_desc = list(dict_quest_desc.keys())[list(
dict_quest_desc.values()).index(curr_state2)]
if not for_training:
return epi_reward
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 \
# initialize for each episode
gamma_step = 1
epi_reward = 0
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
current_state = current_room_desc + current_quest_desc
current_state_vector = utils.extract_bow_feature_vector(
current_state, dictionary)
(action_index, object_index) = epsilon_greedy(current_state_vector,
theta, epsilon)
(next_room_desc, next_quest_desc, reward,
terminal) = framework.step_game(current_room_desc, current_quest_desc,
action_index, object_index)
if for_training:
# update Q-function.
next_state = next_room_desc + next_quest_desc
# Returns the bag-of-words vector representation of the state
next_state_vector = utils.extract_bow_feature_vector(
next_state, dictionary)
linear_q_learning(theta, current_state_vector, action_index,
object_index, reward, next_state_vector,
terminal)
if not for_training:
# update reward
epi_reward = epi_reward + gamma_step * reward
gamma_step = gamma_step * GAMMA
# prepare next step
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
if not for_training:
return epi_reward
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
epi_reward = None
# initialize for each episode
epi_reward = 0
t = 0
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
current_state_1, current_state_2 = dict_room_desc[
current_room_desc], dict_quest_desc[current_quest_desc]
# get the next action according to policy
action_index, object_index = epsilon_greedy(current_state_1,
current_state_2, q_func,
epsilon)
# take action and get the next state
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(
current_room_desc, current_quest_desc, action_index, object_index)
next_state_1, next_state_2 = dict_room_desc[
next_room_desc], dict_quest_desc[next_quest_desc]
if for_training:
# update Q-function.
tabular_q_learning(q_func, current_state_1, current_state_2,
action_index, object_index, reward,
next_state_1, next_state_2, terminal)
pass
if not for_training:
# update reward
epi_reward = epi_reward + GAMMA**t * reward
t = t + 1
pass
# prepare next step
#current_state_1, current_state_2 = next_state_1, next_state_2
current_room_desc, current_quest_desc = next_room_desc, next_quest_desc
if not for_training:
return epi_reward
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
epi_reward = 0
# initialize for each episode
# TODO Your code here
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
counter = 0
while not terminal:
# Choose next action and execute
# TODO Your code here
(action_index, object_index) = epsilon_greedy(dict_room_desc[current_room_desc], dict_quest_desc[current_quest_desc], q_func, epsilon)
(next_room_desc, next_quest_desc, reward, terminal) = framework.step_game(current_room_desc, current_quest_desc, action_index, object_index)
if for_training:
# update Q-function.
tabular_q_learning(q_func, dict_room_desc[current_room_desc], dict_quest_desc[current_quest_desc], action_index,
object_index, reward, dict_room_desc[next_room_desc], dict_quest_desc[next_quest_desc],
terminal)
# TODO Your code here
pass
if not for_training:
# update reward
epi_reward += reward * (GAMMA ** (framework.STEP_COUNT-1))
# TODO Your code here
pass
counter += 1
# prepare next step
# TODO Your code here
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
if not for_training:
return epi_reward
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
epi_reward = 0.0 # initialize for each episode
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
current_state = current_room_desc + current_quest_desc
current_state_vector = utils.extract_bow_feature_vector(
current_state, dictionary)
next_action_index, next_object_index = epsilon_greedy(current_state_vector,
theta,
epsilon) # Get next action, object
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(
current_room_desc,
current_quest_desc,
next_action_index,
next_object_index) # Take a step
next_state = next_room_desc + next_quest_desc # Build next state vector
next_state_vector = utils.extract_bow_feature_vector(next_state, dictionary)
if for_training:
# update Q-function.
linear_q_learning(theta, current_state_vector, next_action_index,
next_object_index, reward, next_state_vector, terminal) # Update theta
if not for_training:
# update reward
epi_reward += (GAMMA**(framework.STEP_COUNT - 1))*reward
# prepare next step
current_room_desc, current_quest_desc = next_room_desc, next_quest_desc
if not for_training:
return epi_reward
def run_episode(for_training):
"""
Runs one episode
If for training, update Q function
If for testing, computes and return cumulative discounted reward
"""
# My solution:
epsilon = TRAINING_EP if for_training else TESTING_EP
epi_reward = 0
# initialize for each episode
current_room_desc, current_quest_desc, terminal = framework.newGame()
t = 0
while not terminal:
# Choose next action and execute
current_state = current_room_desc + current_quest_desc
current_state_vector = torch.FloatTensor(
utils.extract_bow_feature_vector(current_state,
dictionary)).to(device)
action_index, object_index = epsilon_greedy(current_state_vector,
epsilon)
next_room_desc, next_quest_desc, reward, terminal = \
framework.step_game(current_room_desc, current_quest_desc,
action_index, object_index)
next_state = next_room_desc + next_quest_desc
next_state_vector = torch.FloatTensor(
utils.extract_bow_feature_vector(next_state,
dictionary)).to(device)
if for_training:
# update Q-function.
deep_q_learning(current_state_vector, action_index, object_index,
reward, next_state_vector, terminal)
if not for_training:
# update reward
epi_reward += GAMMA**t * reward
t += 1
# prepare next step
current_room_desc, current_quest_desc = next_room_desc, next_quest_desc
if not for_training:
return epi_reward
def run_episode(for_training):
"""
Runs one episode
If for training, update Q function
If for testing, computes and return cumulative discounted reward
"""
epsilon = TRAINING_EP if for_training else TESTING_EP
# initialize for each episode
epi_reward = 0.0
step = 0
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
current_state = current_room_desc + current_quest_desc
current_state_vector = torch.FloatTensor(
utils.extract_bow_feature_vector(current_state, dictionary))
# Get next c
next_action_i, next_object_i = epsilon_greedy(current_state_vector, epsilon)
# Make a move
step += 1
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(
current_room_desc,
current_quest_desc,
next_action_i,
next_object_i)
# Next state vector
next_state = next_room_desc + next_quest_desc
next_state_vector = torch.FloatTensor(utils.extract_bow_feature_vector(next_state, dictionary))
if for_training:
# update Q-function.
deep_q_learning(current_state_vector, next_action_i,
next_object_i, reward, next_state_vector, terminal)
if not for_training:
# update reward
epi_reward += (GAMMA**(step - 1)) * reward
# prepare next step
current_room_desc, current_quest_desc = next_room_desc, next_quest_desc
if not for_training:
return epi_reward
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
# initialize for each episode
epi_reward = 0
step = 0
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
current_room_desc_i = dict_room_desc[current_room_desc]
current_quest_desc_i = dict_quest_desc[current_quest_desc]
# Get next c
next_action_i, next_object_i = epsilon_greedy(current_room_desc_i, current_quest_desc_i, q_func, epsilon)
# Make a move
step += 1
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(current_room_desc, current_quest_desc, next_action_i, next_object_i)
next_room_desc_i = dict_room_desc[next_room_desc]
if for_training:
# update Q-function.
tabular_q_learning(q_func, current_room_desc_i, current_quest_desc_i,
next_action_i, next_object_i, reward,
next_room_desc_i, current_quest_desc_i,
terminal)
if not for_training:
# update reward. sum(gamma^t * reward)
epi_reward += (GAMMA**(step - 1)) * reward
# prepare next step
current_room_desc, current_quest_desc = next_room_desc, next_quest_desc
if not for_training:
return epi_reward
def run_episode(for_training):
""" Runs one episode
If for training, update Q function
If for testing, computes and return cumulative discounted reward
theta & dictionary are freaking global
Args:
for_training (bool): True if for training
Returns:
None
"""
epsilon = TRAINING_EP if for_training else TESTING_EP
epi_reward = 0.
t = 0
# initialize for each episode
# theta = ... # global
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
current_state = current_room_desc + current_quest_desc
current_state_vector = utils.extract_bow_feature_vector(
current_state, dictionary)
action_index, object_index = epsilon_greedy(current_state_vector,
theta, epsilon)
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(
current_room_desc, current_quest_desc, action_index, object_index)
next_state_vector = utils.extract_bow_feature_vector(
next_room_desc + next_quest_desc, dictionary)
if for_training:
# update Q-function.
linear_q_learning(theta, current_state_vector, action_index,
object_index, reward, next_state_vector,
terminal)
if not for_training:
epi_reward += (GAMMA**t) * reward
# prepare next step
t += 1
if not for_training:
return epi_reward
def run_episode(for_training):
"""
Runs one episode
If for training, update Q function
If for testing, computes and return cumulative discounted reward
"""
epsilon = TRAINING_EP if for_training else TESTING_EP
gamma_step = 1
epi_reward = 0
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
current_state = current_room_desc + current_quest_desc
current_state_vector = torch.FloatTensor(
utils.extract_bow_feature_vector(current_state, dictionary))
(action_index, object_index) = epsilon_greedy(current_state_vector,
epsilon)
(next_room_desc, next_quest_desc, reward,
terminal) = framework.step_game(current_room_desc, current_quest_desc,
action_index, object_index)
if for_training:
# update Q-function.
next_state = next_room_desc + next_quest_desc
# 32-bit floating point CPU tensor
next_state_vector = torch.FloatTensor(
utils.extract_bow_feature_vector(next_state, dictionary))
deep_q_learning(current_state_vector, action_index, object_index,
reward, next_state_vector, terminal)
if not for_training:
# update reward
epi_reward = epi_reward + gamma_step * reward
gamma_step = gamma_step * GAMMA
# prepare next step
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
if not for_training:
return epi_reward
def run_episode(for_training):
"""
Runs one episode
If for training, update Q function
If for testing, computes and return cumulative discounted reward
"""
epsilon = TRAINING_EP if for_training else TESTING_EP
epi_reward = 0.0
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
current_state = current_room_desc + current_quest_desc
current_state_vector = torch.FloatTensor(
utils.extract_bow_feature_vector(current_state, dictionary))
next_action_index, next_object_index = epsilon_greedy(
current_state_vector, epsilon)
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(
current_room_desc, current_quest_desc, next_action_index,
next_object_index)
next_state = next_room_desc + next_quest_desc
next_state_vector = torch.FloatTensor(
utils.extract_bow_feature_vector(next_state, dictionary))
if for_training:
deep_q_learning(current_state_vector, next_action_index,
next_object_index, reward, next_state_vector,
terminal)
if not for_training:
epi_reward += (GAMMA**(framework.STEP_COUNT - 1)) * reward
# prepare next step
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
if not for_training:
return epi_reward
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
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
current_state = current_room_desc + current_quest_desc
current_state_vector = utils.extract_bow_feature_vector(
current_state, dictionary)
# TODO Your code here
# Decidir acción con epsilon_greedy
action_index, object_index = epsilon_greedy(current_state_vector,
theta, 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]
next_state = next_room_desc + next_quest_desc
next_state_vector = utils.extract_bow_feature_vector(
next_state, dictionary)
if for_training:
# update Q-function.
# TODO Your code here
linear_q_learning(theta, current_state_vector, action_index,
object_index, reward, next_state_vector,
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
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
"""
""" My solution:
epsilon = TRAINING_EP if for_training else TESTING_EP
epi_reward = 0
# initialize for each episode
current_room_desc, current_quest_desc, terminal = framework.newGame() # string descriptions
t = 0
while not terminal:
# Choose next action and execute
room_index = dict_room_desc[current_room_desc]
quest_index = dict_quest_desc[current_quest_desc]
action_index, object_index = epsilon_greedy(room_index, quest_index,
q_func, epsilon)
next_room_desc, next_quest_desc, reward, terminal = \
framework.step_game(current_room_desc, current_quest_desc,
action_index, object_index)
next_room_index = dict_room_desc[next_room_desc]
next_quest_index = dict_quest_desc[next_quest_desc]
if for_training:
# update Q-function.
tabular_q_learning(q_func, room_index, quest_index,
action_index, object_index,
reward, next_room_index, next_quest_index,
terminal)
if not for_training:
# update reward
epi_reward += GAMMA ** t * reward
t += 1
# prepare next step
current_room_desc, current_quest_desc = next_room_desc, next_quest_desc
if not for_training:
return epi_reward
"""
# Instructor's solution:
# Difference reward section:
# Uses epi_reward += gamma_step * reward, then gamma_step += GAMMA
epsilon = TRAINING_EP if for_training else TESTING_EP
gamma_step = 1
epi_reward = 0
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
while not terminal:
# Choose next action and execute
cur_room_desc_id = dict_room_desc[current_room_desc]
cur_quest_desc_id = dict_quest_desc[current_quest_desc]
(action_index, object_index) = epsilon_greedy(cur_room_desc_id,
cur_quest_desc_id,
q_func, epsilon)
(next_room_desc, next_quest_desc, reward,
terminal) = framework.step_game(current_room_desc, current_quest_desc,
action_index, object_index)
if for_training:
# update Q-function.
next_room_desc_id = dict_room_desc[next_room_desc]
next_quest_desc_id = dict_quest_desc[next_quest_desc]
tabular_q_learning(q_func, cur_room_desc_id, cur_quest_desc_id,
action_index, object_index, reward,
next_room_desc_id, next_quest_desc_id, terminal)
if not for_training:
# update reward
epi_reward = epi_reward + gamma_step * reward
gamma_step = gamma_step * GAMMA
# prepare next step
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
if not for_training:
return epi_reward
def run_episode(for_training, need_history=False):
""" 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
epi_reward = 0
# initialize for each episode
# TODO Your code here
global q_func, dict_room_desc, dict_quest_desc
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
history = list()
while not terminal:
# Choose next action and execute
# get the greedy action wrt the current_room_desc and current_quest_desc
action_index, object_index = epsilon_greedy(
state_1=dict_room_desc[current_room_desc],
state_2=dict_quest_desc[current_quest_desc],
q_func=q_func,
epsilon=epsilon)
# take the action in the environment and get the reward
next_room_desc, next_quest_desc, reward, terminal = framework.step_game(
current_room_desc=current_room_desc,
current_quest_desc=current_quest_desc,
action_index=action_index,
object_index=object_index)
history.append([
current_room_desc, current_quest_desc, action_index, object_index,
next_room_desc, next_quest_desc, reward, terminal
])
if for_training:
# update Q-function.
tabular_q_learning(
q_func=q_func,
current_state_1=dict_room_desc[current_room_desc],
current_state_2=dict_quest_desc[current_quest_desc],
action_index=action_index,
object_index=object_index,
reward=reward,
next_state_1=dict_room_desc[next_room_desc],
next_state_2=dict_quest_desc[next_quest_desc],
terminal=terminal)
if not for_training:
# update reward
epi_reward += np.power(GAMMA, framework.STEP_COUNT) * reward
# prepare next step
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
if not for_training:
if need_history:
return epi_reward, history
else:
return epi_reward
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
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
# initialize for each episode
# TODO Your code here
# Look into framework.py file for hint
# A tuple where the first element is a description of the initial room,
# the second element is a description of the quest for this new game episode, and
# the last element is a Boolean variable with value False implying that the game is not over.
(current_room_desc, current_quest_desc, terminal) = framework.newGame()
# initial value
count = 0
epi_reward = 0
while not terminal:
# Choose next action and execute
current_state = current_room_desc + current_quest_desc
current_state_vector = utils.extract_bow_feature_vector(current_state, dictionary)
# TODO Your code here
(action_index, object_index) = epsilon_greedy(current_state_vector, theta, epsilon)
# Renaming to shorter name
a_idx = action_index # rename for shorter name for action index
o_idx = object_index # rename for shorter name for object index
crd = current_room_desc # rename for shorter name for current room description
cqd = current_quest_desc # rename for shorter name for current quest description
# the system next state when the selected command is applied at the current state
(next_room_desc, next_quest_desc, reward, terminal) = framework.step_game(crd, cqd, a_idx, o_idx)
next_state = next_room_desc + next_quest_desc
# Look into utils.py for the bag-of-words vector representation of the state
next_state_vector = utils.extract_bow_feature_vector(next_state, dictionary)
if for_training:
# update Q-function.
# TODO Your code here
linear_q_learning(theta, current_state_vector, a_idx, o_idx, reward, next_state_vector, terminal)
pass
if not for_training:
# update reward
# TODO Your code here
epi_reward += np.power(GAMMA, count) * reward
pass
# prepare next step
# TODO Your code here
count += 1
current_room_desc = next_room_desc
current_quest_desc = next_quest_desc
if not for_training:
return epi_reward
