def update_chain(self, state, action, reward):
lhstate = listhash(state)
if not lhstate in self.q:
self.q[lhstate] = dict()
if not action in self.q[lhstate]:
self.q[lhstate][action] = reward
else:
val = self.q[lhstate][action]
self.q[lhstate][action] = val + self.learning_rate * \
(reward - self.discount * val)
def update_chain(self, state, action, reward):
lhstate = listhash(state)
if not lhstate in self.q:
self.q[lhstate] = dict()
if not action in self.q[lhstate]:
self.q[lhstate][action] = reward
else:
val = self.q[lhstate][action]
self.q[lhstate][action] = val + self.learning_rate * \
(reward - self.discount * val)
def get_greedy_action(self, available_actions):
# Do a tree search in the previously seen states
# that match the current state
best_action = None
best_value = None
for state in sublists(self.chain):
lhstate = listhash(state)
if lhstate in self.q:
s = self.q[lhstate]
for a in available_actions:
if a in s:
val = s[a]
if val > best_value:
best_action = a
best_value = val
return best_action
def get_greedy_action(self, available_actions):
# Do a tree search in the previously seen states
# that match the current state
best_action = None
best_value = None
for state in sublists(self.chain):
lhstate = listhash(state)
if lhstate in self.q:
s = self.q[lhstate]
for a in available_actions:
if a in s:
val = s[a]
if val > best_value:
best_action = a
best_value = val
return best_action
