def fit(self, neps, tri, perm):
start = datetime.now()
self.epsilon, self.alpha, self.gamma = perm
returns = np.zeros((neps, tri))
for t in range(tri):
self.agent = SGA.sentGenAgent(self.epsilon, self.alpha, self.gamma)
print('Running trial: ', t)
for n in range(neps):
#if n % 100 == 0:
# print('Running episode: ', n)
env = ENV.Environment(self.rewards, self.reward_func)
state = env.prev_state_id
run = 'run'
l = 0
while run == 'run':
action = self.agent.getAction(state)
run, reward = env.getNextState(action)
returns[n, t] += (self.gamma**l) * reward
l += 1
qsa, phisa = self.agent.qValue(state, action)
next_state = env.next_state_id
max_qsa_prime = []
for act in self.agent.actions.keys():
if run == 'terminate':
qsa_prime = 0.0
else:
qsa_prime, _ = self.agent.qValue(next_state, act)
max_qsa_prime.append(qsa_prime)
qsa_prime = max(max_qsa_prime)
self.agent.updateWeights(reward, qsa_prime, qsa, phisa)
state = next_state
#print returns[:,t]
end = datetime.now()
return returns, end - start
def predict(self):
sentences = []
for n in range(1000):
sent = []
env = ENV.Environment(self.rewards, self.reward_func)
state = env.prev_state_id
sent.append(env.prev_state)
run = 'run'
while run == 'run':
action = self.agent.getAction(state)
run, reward = env.getNextState(action)
next_state = env.next_state
sent.append(next_state)
state = env.next_state_id
sentences.append(sent)
return self.score(sentences)