def backup_on_minibatch(self, q_network, backup_minibatch):
X = np.array([self.delegate.construct_features(state, action)[0]
for state, action, target in backup_minibatch])
Y_info = [(action, target) for _state, action, target in backup_minibatch]
Y = q_network.predict_on_batch(X)
assert len(Y) == len(Y_info)
for y, (action, target) in zip(Y, Y_info): y[action_index(action)] = target
loss = q_network.train_on_batch(X, Y)
def backup_on_minibatch(self, backup_minibatch):
X = np.array([self.delegate.construct_features(state, action)[0]
for state, action, target in backup_minibatch])
Y_info = [(action, target) for _state, action, target in backup_minibatch]
Y = self.delegate.model.predict_on_batch(X)
assert len(Y) == len(Y_info)
for y, (action, target) in zip(Y, Y_info): y[action_index(action)] = target
loss = self.delegate.model.train_on_batch(X, Y)
self.delegate.loss_history.append(loss)
self.delegate.prediction_cache = (None, None)
def predict_value_by_network(self, network, state, action):
X, action = self.delegate.construct_features(state, action)
values = network.predict_on_batch(np.array([X]))[0].tolist()
valur_for_action = values[action_index(action)]
return valur_for_action
