def __init__(self,
domain: Domain,
task: Task,
epsilon=0.1,
alpha=0.6,
gamma=0.95,
lamb=0.95,
expected=False):
"""
:param domain: The world the agent is placed in.
:param task: The task in the world, which defines the reward function.
"""
super().__init__(domain, task)
self.world = domain
self.task = task
self.epsilon = epsilon
self.alpha = alpha
self.gamma = gamma
self.lamb = lamb
self.expected = expected
self.previousaction = None
self.previousstate = None
self.value_old = 0.0
example_state = domain.get_current_state()
actions = domain.get_actions(example_state)
self.feature_extractor = DiscretizedGridWorldState(
example_state, GridWorldAction(Direction.up))
self.eligibility = np.zeros(self.feature_extractor.number_of_features)
self.value_function = LinearVFA(
self.feature_extractor.number_of_features, actions)
self.current_cumulative_reward = 0.0
def __init__(self,
domain: Domain,
task: Task,
epsilon=0.1,
alpha=0.6,
gamma=0.95,
lamb=0.95,
expected=False):
"""
:param domain: The world the agent is placed in.
:param task: The task in the world, which defines the reward function.
"""
super().__init__(domain, task)
self.world = domain
self.task = task
self.epsilon = epsilon
self.alpha = alpha
self.gamma = gamma
self.lamb = lamb
self.expected = expected
self.previousaction = None
self.previousstate = None
self.value_function = StateActionValueTable(
domain.get_actions(domain.get_current_state()))
self.current_cumulative_reward = 0.0
def __init__(self,
domain: Domain,
task: Task,
feature_extractor: FeatureExtractor,
epsilon=0.1,
alpha=0.6,
gamma=1.0,
name="Sarsa"):
"""
:param domain: The world the agent is placed in.
:param task: The task in the world, which defines the reward function.
"""
super().__init__(domain, task, name)
self.world = domain
self.task = task
self.epsilon = epsilon
self.alpha = alpha
self.gamma = gamma
self.previousaction = None
self.previousstate = None
actions = domain.get_actions()
self.feature_extractor = feature_extractor
self.value_function = CMACValueFunction(
self.feature_extractor.num_features(), actions, alpha)
self.current_cumulative_reward = 0.0
def __init__(self,
domain: Domain,
task: Task,
feature_extractor: FeatureExtractor,
epsilon=0.5,
alpha=0.1,
gamma=1.0,
value_function=None):
self.initial_epsilon = epsilon
self.epsilon = epsilon
self.alpha = alpha
self.gamma = gamma
self.feature_extractor = feature_extractor
num_actions = len(domain.get_actions())
num_state_features = feature_extractor.num_features()
if value_function is None:
self.value_function = QNetwork(num_actions, num_state_features,
self.alpha)
else:
self.value_function = value_function
self.world = domain
self.task = task
self.current_cumulative_reward = 0.0
self.previousaction = None
self.previousstate = None
self.name = "Q-network"
def __init__(self, domain: Domain, task: Task, feature_extractor: FeatureExtractor, epsilon=0.1, alpha=0.5,
gamma=1.0, value_function=None):
self.initial_epsilon = epsilon
self.epsilon = epsilon
self.alpha = alpha / domain.history_length
self.gamma = gamma
self.feature_extractor = feature_extractor
num_actions = len(domain.get_actions())
num_state_features = feature_extractor.num_features()
if value_function is None:
self.value_function = DoubleQNetworkValueFunction(num_actions, num_state_features, self.alpha)
else:
self.value_function = value_function
self.world = domain
self.task = task
self.current_cumulative_reward = 0.0
self.previousaction = None
self.previousstate = None
self.name = "Double Dueling DQN"
self.experience_buffer = ExperienceBuffer()
self.total_steps = 0
self.update_frequency = 4
self.batch_size = 32
self.pretraining_steps = 100
def __init__(self, domain: Domain, task: Task, epsilon=0.1, alpha=0.6, gamma=1.0, lamb=0.95, expected=False,
feature_extractor=None,
name="Sarsa"):
"""
:param domain: The world the agent is placed in.
:param task: The task in the world, which defines the reward function.
"""
super().__init__(domain, task, name)
self.world = domain
self.task = task
self.epsilon = epsilon
self.alpha = alpha
self.gamma = gamma
self.lamb = lamb
self.expected = expected
self.previousaction = None
self.previousstate = None
self.value_function = StateActionValueTable(domain.get_actions())
self.current_cumulative_reward = 0.0
self.eligibility = {}
self.v_old = 0
def __init__(self, domain: Domain, task: Task, feature_extractor: FeatureExtractor, epsilon=0.1, alpha=0.6,
gamma=1.0, lamb=0.95, name="True Online Sarsa(λ)"):
"""
:param domain: The world the agent is placed in.
:param task: The task in the world, which defines the reward function.
"""
super().__init__(domain, task, name)
self.world = domain
self.task = task
self.epsilon = epsilon
self.alpha = alpha
self.gamma = gamma
self.lamb = lamb
self.previousaction = None
self.previousstate = None
self.value_old = 0.0
actions = domain.get_actions()
self.feature_extractor = feature_extractor
self.eligibility = np.zeros(self.feature_extractor.num_features())
self.value_function = LinearVFA(self.feature_extractor.num_features(), actions)
self.current_cumulative_reward = 0.0