def react(self, h, a):
happy_ratio = h.stats.get(type(self).__name__, dict()).get(':)', 0.0)
self.sm.hm.record([self.sm.state])
if a == self.optimal_actions[self.sm.state]:
e_next = grl.epsilon_sample(self.pm.percept_space, ':)', 1 - happy_ratio)
if e_next == ':)':
self.sm.state = (self.sm.state + 1) % len(self.sm.state_space)
else:
e_next = ':('
return e_next
def react(self, h, a):
bang_ratio = h.stats.get(type(self).__name__, dict()).get('#', self.pmin)
self.sm.hm.record([self.sm.state])
if a == self.optimal_actions[self.sm.state]:
e_next = grl.epsilon_sample(self.pm.percept_space, '#', 1 - bang_ratio)
if (e_next == '#' and self.sm.state == 0) or (e_next == '@' and self.sm.state == 1):
self.sm.state = (self.sm.state + 1) % len(self.sm.state_space)
else:
self.sm.state = 0
e_next = '@'
return e_next
def act(self, h):
self.pi, self.v = grl.PITabular(self.p,
self.r,
self.v,
self.pi,
g=self.g,
steps=1,
vi_steps=1)
# Oracle Alert!
s = self.hm.state(h, g=self.g, q_func=self.oracle)
return grl.epsilon_sample(self.am.action_space, self.pi[s].argmax(),
self.xpl)
def reset(self):
self.sm.state = grl.epsilon_sample(self.sm.state_space)
def setup(self):
self.sm.state_space = ['s-left', 's-right']
self.am.action_space = ['left', 'right']
self.sm.state = grl.epsilon_sample(self.sm.state_space)
def act(self, h):
return grl.epsilon_sample(self.am.actions)
def act(self, h):
s = self.hm.state(h)
self.am.action = grl.epsilon_sample(self.am.action_space,
max(self.Q[s])[1], 0.1)
return self.am.action
def start(self, e=None, order=1):
self.order = order
self.am.action = grl.epsilon_sample(self.am.action_space)
return self.am.action
