def __init__(self,
actions,
rand_init=True,
name="Linear-Q",
alpha=0.001,
gamma=0.99,
epsilon=0.2,
explore="uniform",
feature=None,
anneal=True,
sarsa=False):
# name = name + "-rbf" if rbf else name
QLearningAgent.__init__(self,
actions=list(actions),
name=name,
alpha=alpha,
gamma=gamma,
epsilon=epsilon,
explore=explore,
anneal=anneal)
self.sarsa = sarsa
self.feature = feature # function (state, action) -> features (numpy vector)
self.num_features = self.feature.num_features()
# Add a basis feature.
self.rand_init = rand_init
if rand_init:
self.weights = np.random.random(self.num_features *
len(self.actions))
else:
self.weights = np.zeros(self.num_features * len(self.actions))
self.max_weight = 0.0
def __init__(self,
actions,
num_features,
rand_init=True,
name="Linear-Q",
alpha=0.2,
gamma=0.99,
epsilon=0.2,
explore="uniform",
rbf=False,
anneal=True):
name = name + "-rbf" if rbf else name
QLearningAgent.__init__(self,
actions=list(actions),
name=name,
alpha=alpha,
gamma=gamma,
epsilon=epsilon,
explore=explore,
anneal=anneal)
self.num_features = num_features
self.rand_init = rand_init
# Add a basis feature.
if rand_init:
self.weights = np.random.random(self.num_features *
len(self.actions))
else:
self.weights = np.zeros(self.num_features * len(self.actions))
self.rbf = rbf
def __init__(self, actions, num_features, rand_init=True, name="Linear-Q", alpha=0.2, gamma=0.99, epsilon=0.2, explore="uniform", anneal=True):
QLearningAgent.__init__(self, actions=list(actions), name=name, alpha=alpha, gamma=gamma, epsilon=epsilon, explore=explore, anneal=anneal)
self.num_features = num_features
self.rand_init = rand_init
# Add a basis feature.
if rand_init:
self.weights = np.random.random(self.num_features*len(self.actions))
else:
self.weights = np.zeros(self.num_features*len(self.actions))