def observe(self, observation):
"""Update state, given a previous reply by other agent. In case of
questioner, it can be goal description at start of episode.
"""
self.observation = observation
# if episode not done, tokenize, embed and update state
# at the end of dialog episode, perform backward pass and step
if not observation.get('episode_done', False):
text_tokens = self.tokenize(observation['text'])
token_embeds = self.listen_net(text_tokens)
if 'image' in observation:
token_embeds = torch.cat((token_embeds, observation['image']), 1)
token_embeds = token_embeds.squeeze(1)
self.h_state, self.c_state = self.state_net(token_embeds,
(self.h_state, self.c_state))
else:
if observation.get('reward', None):
for action in self.actions:
action.reinforce(observation['reward'])
autograd_backward(self.actions, [None for _ in self.actions],
retain_graph=True)
# clamp all gradients between (-5, 5)
for module in self.modules:
for parameter in module.parameters():
parameter.grad.data.clamp_(min=-5, max=5)
optimizer.step()
else:
# start of dialog episode
optimizer.zero_grad()
self.reset()
def observe(self, observation):
"""Update state, given a previous reply by other agent. In case of
questioner, it can be goal description at start of episode.
"""
self.observation = observation
# if episode not done, tokenize, embed and update state
# at the end of dialog episode, perform backward pass and step
if not observation.get('episode_done', False):
text_tokens = self.tokenize(observation['text'])
token_embeds = self.listen_net(text_tokens)
if 'image' in observation:
token_embeds = torch.cat((token_embeds, observation['image']),
1)
token_embeds = token_embeds.squeeze(1)
self.h_state, self.c_state = self.state_net(
token_embeds, (self.h_state, self.c_state))
else:
if observation.get('reward', None):
for action in self.actions:
action.reinforce(observation['reward'])
autograd_backward(self.actions, [None for _ in self.actions],
retain_graph=True)
# clamp all gradients between (-5, 5)
for module in self.modules:
for parameter in module.parameters():
parameter.grad.data.clamp_(min=-5, max=5)
optimizer.step()
else:
# start of dialog episode
optimizer.zero_grad()
self.reset()
def observe(self, observation):
"""Given an input token, interact for next round."""
self.observation = observation
if not observation.get('episode_done'):
# embed and pass through LSTM
token_embeds = self.listen_net(observation['text'])
# concat with image representation (valid for abot)
if 'image' in observation:
token_embeds = torch.cat((token_embeds, observation['image']),
1)
# remove all dimensions with size one
token_embeds = token_embeds.squeeze(1)
# update agent state using these tokens
self.h_state, self.c_state = self.rnn(token_embeds,
(self.h_state, self.c_state))
else:
if observation.get('reward') is not None:
for action in self.actions:
action.reinforce(observation['reward'])
autograd_backward(self.actions, [None for _ in self.actions],
retain_graph=True)
# clamp all gradients between (-5, 5)
for parameter in self.parameters():
parameter.grad.data.clamp_(min=-5, max=5)