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function_approximation.py
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function_approximation.py
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import numpy as np
from sklearn import linear_model
from sklearn.svm import SVR
from sklearn import tree
from sklearn.linear_model import SGDRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.utils import shuffle
from sklearn.kernel_approximation import RBFSampler
import sklearn.pipeline
class FunctionApproximation:
'''
Training algorithm: Variety of algorithms (linear, ridge, tree based, kernelized) used for representing the Q(s,a).
Feature Extraction: Construct kernelized features (RBF, Fourier etc) from state variables for capturing non-linearity
Batches: FittedQIteration alternates between constructing batches of experience and then fitting the Q-function.
Note: parameters/weights correspond to the chosen action in the given state
'''
def __init__(self, name, actions):
self.debug = True
self.actions = actions
self.initial_data = np.array([[3, 3.80, 0.40, 1.50], [3.0783744000000004, 1.7999999999999998, 0.04, 2.5],
[3.6603792000000004, 5.8500000000000005, 0.08, -1.5], [2.8383936000000003, 5.8500000000000005, 0.04, -3.0],
[4.5679104000000015, 5.8500000000000005, 0.04, -2.0], [2.885976, 4.05, 0.04, 1.0]])
self.initial_labels = np.array([[-1.0], [-0.2], [-0.5], [-.25], [0.0], [-2.1]])
self.model_name = str(name)
self.observation_samples = np.array([self.sample_states() for obv in range(10000)])
self.featurizer = sklearn.pipeline.FeatureUnion([("rbf1", RBFSampler(gamma=5.0, n_components=100)),
("rbf2", RBFSampler(gamma=2.0, n_components=100)),
("rbf3", RBFSampler(gamma=1.0, n_components=100)),
("rbf4", RBFSampler(gamma=0.5, n_components=100))])
self.featurizer.fit(self.observation_samples)
self.feature_scaler = StandardScaler()
self.feature_scaler.fit(self.observation_samples)
if self.model_name == 'svr':
self.model = SVR(kernel='rbf')
#self.model.fit(self.initial_data, self.initial_labels)
elif self.model_name == 'extra_trees':
self.model = ExtraTreesRegressor().fit(self.initial_data, self.initial_labels)
elif self.model_name == 'sgd':
self.models = {}
for a in range(len(self.actions)):
model = SGDRegressor(learning_rate="constant")
model.partial_fit([ self.featurize_state([3.6603792000000004, 2.8500000000000005, 0.08]) ], [0])
self.models[a] = model
#self.model = SGDRegressor(penalty='none')
#self.model.fit(self.feature_scaler.transform(self.initial_data), self.initial_labels)
else:
self.model = None
def sample_states(self):
'''
state = [netload, energy, price]
'''
load_sample = np.random.uniform(low=-2.6, high=6.0)
energy_sample = np.random.uniform(low=1.7, high=6.0)
price_sample = np.random.uniform(low=0.0, high=0.12)
sample = [load_sample, energy_sample, price_sample]
return sample
def featurize_state(self, state):
'''
1. Normalize the observation/state-space with zero mean and unit variance.
2. Return RBF kernelzied features #CHECK SUTTON BARTO SECTION for intuition
'''
scaled = self.feature_scaler.transform(state)
featurized = self.featurizer.transform(scaled)
return featurized[0]
def update_qfunction(self, state, action_index, qvalue):
features = self.featurize_state(state)
self.models[action_index].partial_fit([features], [qvalue])
return None
'''
def update_qfunction(self, minibatch, agent_instance):
#Update the model/estimator/FA for each action.
minibatch = np.array(minibatch) # (netload, energy, price, qvalue, action_index)
groups = np.split(minibatch, np.where(np.diff(minibatch[:,4]))[0]+1) #returns a list with group of array elements
actions = np.unique([x[0][4]for x in groups]) # unqiue actions
print len(actions)
for i, action_index in enumerate(actions):
features = self.featurize_state(groups[i][:,:3])
qvalue = groups[i][:,3:4]
#print 'features', features.shape
#print 'qvalues', qvalue
#print 'obvs', self.observation_samples.shape
self.models[action_index].partial_fit([features], qvalue)
'''
def predictQvalue(self, state, agent_instance, legal_actions):
features = self.featurize_state(state)
#print'predict', features.shape
if len(legal_actions) != 1:
pred = np.array([ self.models[key].predict([features])[0] for key in legal_actions])
return np.max(pred)
else:
key = legal_actions[0]
return self.models[key].predict([features])[0]
'''
def predictQvalue(self, state, agent_instance, legal_actions):
#stateaction = numpy.zeros((len(self.actions), len(state)))
#stateaction[action,:] = state
qvalues = []
if len(legal_actions) == 1:
action_index = legal_actions[0]
action = agent_instance.actions[action_index]
features = state + [action]
features = np.array(features)
features_scaled = self.feature_scaler.transform(features)
qvalue = self.model.predict(features)
#qvalue = self.model.predict(features_scaled)
return qvalue[0]
else:
#print 'not inside 1:', len(legal_actions)
for action_index in legal_actions:
action = agent_instance.actions[action_index]
features = state + [action]
features = np.array(features)
features_scaled = self.feature_scaler.transform(features)
prediction = self.model.predict(features)
#prediction = self.model.predict(features_scaled)
qvalues.append(prediction[0].tolist())
if len(qvalues) == 0:
print 'Zero length, state', state
return np.max(qvalues)
def update_qfunction(self, minibatch, agent_instance):
#print 'Weights before update',self.model.coef_
X_train = [] #features (states? actions? both?)
y_train = [] #qvalues
#Loop through the batch and create the training set.
for memory in minibatch:
currentStateAction, kQvalue = memory
#Get prediction of Q(currentState, currentAaction)
#Ideally input features???
X_train.append(np.asarray(currentStateAction))
y_train.append(np.asarray(kQvalue))
X_train = np.array(X_train)
y_train = np.array(y_train).reshape(400,1)
#print X_train.shape, y_train.shape
#Shuffling for stochastic gradient descent and breaking correlations
X_train, y_train = shuffle(X_train, y_train)
#print X_train, y_train
#Standardize the features
X_scaled = self.feature_scaler.fit_transform(X_train)
#Now update the model or Q-function
#self.model.partial_fit(X_scaled, y_train)
self.model.fit(X_train, y_train)
#self.model.fit(X_scaled, y_train)
'''