class Actor(object):
def __init__(self, n_st, n_act):
super(Actor, self).__init__()
self.n_st = n_st
self.n_act = n_act
self.model = NN(n_st, n_act)
self.optimizer = optimizers.Adam()
self.optimizer.setup(self.model)
self.noise = ou_process(np.zeros((n_act), dtype=np.float32))
def action(self, st, noise=False):
a = self.model(st, norm=True)
if noise:
n = next(self.noise)
a = np.clip(a.data + n, -1, 1)
return a
else:
return a.data
def update(self, st, dqda):
mu = self.model(st, norm=True)
self.model.cleargrads()
mu.grad = -dqda
mu.backward()
self.optimizer.update()
def update_target(self, tau, current_NN):
self.model.weight_update(tau, current_NN)
def save_model(self, outputfile):
serializers.save_npz(outputfile, self.model)
def load_model(self, inputfile):
serializers.load_npz(inputfile, self.model)
class Critic(object):
def __init__(self, n_st, n_act):
super(Critic, self).__init__()
self.n_st = n_st
self.n_act = n_act
self.model = NN(n_st + n_act, 1)
self.optimizer = optimizers.Adam()
self.optimizer.setup(self.model)
self.log = []
def Q_value(self, st, act):
state_action_vector = np.concatenate((st, act), axis=1)
Q = self.model(state_action_vector).data
return Q
def return_dqda(self, st, act):
state_action_vector = Variable(np.concatenate((st, act), axis=1))
self.model.cleargrads()
Q = self.model(state_action_vector)
Q.grad = np.ones((state_action_vector.shape[0], 1), dtype=np.float32)
Q.backward()
grad = state_action_vector.grad[:, self.n_st:]
return grad
def update(self, y, st, act):
self.model.cleargrads()
state_action_vector = np.concatenate((st, act), axis=1)
Q = self.model(state_action_vector)
loss = F.mean_squared_error(Q, Variable(y))
loss.backward()
self.optimizer.update()
self.log.append('Q:{0},y:{1}\n'.format(Q.data.T, y.T))
return loss.data
def update_target(self, tau, current_NN):
self.model.weight_update(tau, current_NN)
def save_model(self, outputfile):
serializers.save_npz(outputfile, self.model)
def load_model(self, inputfile):
serializers.load_npz(inputfile, self.model)
