def __init__(self,
env,
b_agent,
include_utterance_in_observation=False,
*args,
**kwargs):
"""
Args:
env: Environment which is being wrapped by this conversational
environment. This environment must have a discrete action
space. The RL agent will be able to take any of the actions
in this environment, or select a new action `utterance`,
managed by this class.
b_agent: `Agent` instance with which the learned agent will
interact.
include_utterance_in_observation: If `True`, after making an
utterance, include the uttered token as part of the next
observation
"""
super(SituatedConversationEnvironment, self).__init__(*args, **kwargs)
assert isinstance(env.action_space, Discrete)
self._env = env
self.num_tokens = b_agent.num_tokens
self.vocab = b_agent.vocab
self.vocab_size = len(self.vocab)
# Observations are a combination of observations from the wrapped
# environment and a representation of any utterance received from the
# agent.
#
# Optionally include a single-token utterance observation as well.
self._received_message_space = DiscreteBinaryBag(self.vocab_size)
if include_utterance_in_observation:
utterance_space = Discrete(self.vocab_size)
self._obs_space = Product(env.observation_space,
self._received_message_space,
utterance_space)
else:
self._obs_space = Product(env.observation_space,
self._received_message_space)
self.include_utterance_in_observation = include_utterance_in_observation
# The agent can choose to take any action in the wrapped env, to add a
# single token to its message, or to send a message to the agent.
#
# First `N` actions correspond to taking an action in the wrapped env.
# Next `V` actions correspond to uttering a word from the vocabulary.
# Final action corresponds to sending the message.
action_space = Discrete(env.action_space.n + b_agent.vocab_size + 1)
self._action_space = action_space
self.b_agent = b_agent
def to_tf_space(space):
if isinstance(space, TheanoBox):
return Box(low=space.low, high=space.high)
elif isinstance(space, TheanoDiscrete):
return Discrete(space.n)
elif isinstance(space, TheanoProduct):
return Product(list(map(to_tf_space, space.components)))
else:
raise NotImplementedError
def to_tf_space(space):
if isinstance(space, TheanoBox):
return Box(low=space.low, high=space.high)
elif isinstance(space, TheanoDiscrete):
return Discrete(space.n)
elif isinstance(space, TheanoProduct):
return Product(list(map(to_tf_space, space.components)))
else:
print("HACK IN sandbox/rocky/envs/base.py")
return Box(low=space.low, high=space.high)
def __init__(self, env=None, b_agent=None, *args, **kwargs):
"""
Args:
env: Environment which is being wrapped by this conversational
environment. This environment must have a discrete action
space. The RL agent will be able to take any of the actions
in this environment, or select a new action `utterance`,
managed by this class.
b_agent: `Agent` instance with which the learned agent will
interact.
"""
super(SituatedConversationEnvironment, self).__init__(*args, **kwargs)
if env is None:
env = SlaveGridWorldEnv("walled_chain")
if b_agent is None:
b_agent = GridWorldMasterAgent(env)
assert isinstance(env.action_space, Discrete)
self._env = env
self.num_tokens = b_agent.num_tokens
self.vocab = b_agent.vocab
self.vocab_size = len(self.vocab)
# Observations are a combination of observations from the wrapped
# environment and a representation of any utterance received from the
# agent.
self._received_message_space = DiscreteBinaryBag(self.vocab_size)
self._obs_space = Product(env.observation_space,
self._received_message_space)
# The agent can choose to take any action in the wrapped env, to add a
# single token to its message, or to send a message to the agent.
#
# First `N` actions correspond to taking an action in the wrapped env.
# Next `V` actions correspond to uttering a word from the vocabulary.
# Final action corresponds to sending the message.
action_space = Discrete(env.action_space.n + b_agent.vocab_size + 1)
self._action_space = action_space
self._b_agent = b_agent
