def update_weights_continuous(self, s, a, r, ns, eta, gamma=1):
"""
Update weights given state, action, reward and next state using delta rule update
s, a, r, ns: state, action, reward, next state
eta: learning rate
gamma: discount factor
"""
# (neuronindex/popsize)/num_actions = stateindex
# (neuronindex/popsize)%num_actions = actionindex
# stateindex/17 = positionindex
# stateindex%17 = velocityindex
p, v = s
pp, vv = ns
Swphi = np.dot((self.W + self.competition).T[:, :-1],
np.array([0 if (a != i / self.pop_size % 3) else
cf.phi((p - i / self.pop_size / (3 * self.num_v) + 8) % 16 - 8) *
cf.phi(v - (i / self.pop_size / 3) % self.num_v)
for i in xrange(self.K - 1)]))
Intphia = [cf.phi((pp - i / self.pop_size / (3 * self.num_v) + 8) % 16 - 8) *
cf.phi(vv - (i / self.pop_size / 3) % self.num_v) * gamma
for i in xrange(self.K - 1)]
Intphia += [r]
factor = eta * (np.array(Intphia) - Swphi)
for i in xrange(self.num_states):
# skip update for neurons 'far away'
if abs(i / self.num_v - s[0]) > 2 or abs(i % self.num_v - s[1]) > 2:
continue
for k in xrange(self.pop_size):
self.W[self.sa[i, a, k]] += factor *\
cf.phi((p - i / self.num_v + 8) % 16 - 8) *\
cf.phi(v - i % self.num_v)
def get_Q(self, x, p, v):
return np.dot(np.mean((x * (x > 0))[self.sa], axis=2).T, np.array([cf.phi((p - i / self.num_v + 8) % 16 - 8) *
cf.phi(v - i % self.num_v) for i in xrange(self.num_states)]))
