def __init__(self, n='bot', c=False, sc=COBALTGREEN, sb=GOLDENROD, r=20, p=10, a=-15, g=[50,-10,30,20,35,100,30]):
Snake.__init__(self, n, c, sc, sb)
self._dirs_names = [LEFT, UP, RIGHT, DOWN]
self._dirs_ids = {LEFT: 0, UP: 1, RIGHT: 2, DOWN: 3}
self._input_vec = np.zeros(methods._nd_array_grid.shape[0]*methods._nd_array_grid.shape[1] + 4)
self.reinforcement = 0.0
self._iterations_num = 0
# init MPF hierarchy
self.UNIT_VF = (4, 4)
UNIT_INP_DIM = self.UNIT_VF[0]*self.UNIT_VF[1]
self._l0_units = []
self._grid_map = []
for i in xrange(mth._nd_array_grid.shape[0] / self.UNIT_VF[0]):
for j in xrange(mth._nd_array_grid.shape[1] / self.UNIT_VF[1]):
self._l0_units.append(MPF_Unit_RL(
ss_class = Miller_SOM,
ts_class = Miller_SOM,
input_dim = UNIT_INP_DIM,
ss_shape = (10, 10),
ts_shape = (8, 8),
parent_unit = None,
unit_type = MPF_UT_SENSOR
)
)
for x in xrange(i*self.UNIT_VF[0], (i+1)*self.UNIT_VF[0]):
for y in xrange(j*self.UNIT_VF[1], (j+1)*self.UNIT_VF[1]):
self._grid_map.append(x*mth._nd_array_grid.shape[1] + y)
#print self._grid_map
self._l0_units.append(MPF_Unit_RL(
ss_class = Miller_SOM,
ts_class = Miller_SOM,
input_dim = 4,
ss_shape = (1, 4),
ts_shape = (1, 10),
parent_unit = None,
unit_type = MPF_UT_ACTUATOR)
)
#self._l0_units[-1].ss.neurons += 1.0
#self._l0_units[-1].ss.neurons *= 4.0
#self._l0_units[-1].ss.neurons[:] = linspace(0.0, 4.0,
# self._l0_units[-1].ss.neurons.shape[0])[:, None]#array([0.5, 1.5, 2.5, 2.5])[:, None]
#self._l0_units[-1].ss.init_generative_gmm('full')
#for unit in self._l0_units:
#unit.ts.init_generative_gmm('tied')
self._mpf_h = MPF_Hierarchy(self._l0_units, 4, 20, "K:/__temp/mpf_rl_dumps")
#self._mpf_h.top_unit.ss.init_generative_gmm('full')
#self._mpf_h.top_unit.ts.init_generative_gmm('full')
#for unit in self._mpf_h.top_unit.children_units:
#unit.ss.init_generative_gmm('full')
# initial direction
self.direction = self._dirs_names[1]
class OpponentMPF(Opponent):
def __init__(self, n='bot', c=False, sc=COBALTGREEN, sb=GOLDENROD, r=20, p=10, a=-15, g=[50,-10,30,20,35,100,30]):
Snake.__init__(self, n, c, sc, sb)
self._dirs_names = [LEFT, UP, RIGHT, DOWN]
self._dirs_ids = {LEFT: 0, UP: 1, RIGHT: 2, DOWN: 3}
self._input_vec = np.zeros(methods._nd_array_grid.shape[0]*methods._nd_array_grid.shape[1] + 4)
self.reinforcement = 0.0
self._iterations_num = 0
# init MPF hierarchy
self.UNIT_VF = (4, 4)
UNIT_INP_DIM = self.UNIT_VF[0]*self.UNIT_VF[1]
self._l0_units = []
self._grid_map = []
for i in xrange(mth._nd_array_grid.shape[0] / self.UNIT_VF[0]):
for j in xrange(mth._nd_array_grid.shape[1] / self.UNIT_VF[1]):
self._l0_units.append(MPF_Unit_RL(
ss_class = Miller_SOM,
ts_class = Miller_SOM,
input_dim = UNIT_INP_DIM,
ss_shape = (10, 10),
ts_shape = (8, 8),
parent_unit = None,
unit_type = MPF_UT_SENSOR
)
)
for x in xrange(i*self.UNIT_VF[0], (i+1)*self.UNIT_VF[0]):
for y in xrange(j*self.UNIT_VF[1], (j+1)*self.UNIT_VF[1]):
self._grid_map.append(x*mth._nd_array_grid.shape[1] + y)
#print self._grid_map
self._l0_units.append(MPF_Unit_RL(
ss_class = Miller_SOM,
ts_class = Miller_SOM,
input_dim = 4,
ss_shape = (1, 4),
ts_shape = (1, 10),
parent_unit = None,
unit_type = MPF_UT_ACTUATOR)
)
#self._l0_units[-1].ss.neurons += 1.0
#self._l0_units[-1].ss.neurons *= 4.0
#self._l0_units[-1].ss.neurons[:] = linspace(0.0, 4.0,
# self._l0_units[-1].ss.neurons.shape[0])[:, None]#array([0.5, 1.5, 2.5, 2.5])[:, None]
#self._l0_units[-1].ss.init_generative_gmm('full')
#for unit in self._l0_units:
#unit.ts.init_generative_gmm('tied')
self._mpf_h = MPF_Hierarchy(self._l0_units, 4, 20, "K:/__temp/mpf_rl_dumps")
#self._mpf_h.top_unit.ss.init_generative_gmm('full')
#self._mpf_h.top_unit.ts.init_generative_gmm('full')
#for unit in self._mpf_h.top_unit.children_units:
#unit.ss.init_generative_gmm('full')
# initial direction
self.direction = self._dirs_names[1]
def ressurect(self):
if (not self.alive):
self.alive = True
self.coords = mth.getStartCoords(1)
self.direction = self._dirs_names[1]
#self.coords = mth.getStartCoords(random.randint(1, 4))
#self.direction = self._dirs_names[random.randint(1, 4)]
self.reinforcement -= 1.0
self._iterations_num = 0
def __sample_from_pmf(self, pmf, nominals):
# normalize PMF to make it "proper"
abs(pmf, out = pmf)
# -- CDF
bins = cumsum(pmf)
if (bins[-1] == 0.0):
print pmf
raise ValueError
bins /= bins[-1] # normalization
return nominals[digitize(random.random_sample(1), bins)]
def updateDirection(self, grid):
# map grid to linear input vector
self._input_vec[:-4] = grid.flatten()[self._grid_map]
# actuator value from _really_ taken action
self._input_vec[-4:] = 0.0
self._input_vec[-4 + self._dirs_ids[self.direction]] = 1.0
# evaluate hierarchy
res_dir_distr = self._mpf_h.evaluate(self._input_vec, self.reinforcement)
if (self._iterations_num / 20 > 0) and (self._iterations_num % 20 == 0):
print "\n !!! Stil alive for %d !!!\n" % (self._iterations_num)
self.reinforcement += 0.1
if (self.reinforcement > 1.0): self.reinforcement = 1.0
# decode actuator's value
self.direction = self._dirs_names[self.__sample_from_pmf(res_dir_distr, self._dirs_ids.values())]
print res_dir_distr, self.direction, self._mpf_h.reinforcement_prime
self._iterations_num += 1
def apply_reinforcement(self, grid, reinforcement):
"""
print "\n========== PUNISHMENT ============="
#for i in xrange(15):
#self.reinforcement -= 0.9 / 15.0
#if (self.reinforcement < -1.0): self.reinforcement = -1.0
# map grid to linear input vector
self._input_vec[:-4] = grid.flatten()[self._grid_map]
# actuator value from _really_ taken action
self._input_vec[-4:] = 0.0
self._input_vec[-4 + self._dirs_ids[self.direction]] = 1.0
# evaluate hierarchy
self.reinforcement = reinforcement
res_dir_distr = self._mpf_h.evaluate(self._input_vec, reinforcement)
print res_dir_distr, self.direction, self._mpf_h.reinforcement_prime
print "========== END OF PUNISHMENT =============\n"
"""
pass