def __init__(self, env, session, serializer, show_world=False):
super(ConsoleView, self).__init__(env, session)
# for visualization purposes, we keep an internal buffer of the
# input and output stream so when they are cleared from task to
# task, we can keep the history intact.
self.input_buffer = ''
self.output_buffer = ''
self.panic = 'SKIP'
# record what the learner says
self._learner_channel = InputChannel(serializer)
# record what the environment says
self._env_channel = InputChannel(serializer)
# listen to the updates in these channels
self._learner_channel.sequence_updated.register(
self.on_learner_sequence_updated)
self._learner_channel.message_updated.register(
self.on_learner_message_updated)
self._env_channel.sequence_updated.register(
self.on_env_sequence_updated)
self._env_channel.message_updated.register(self.on_env_message_updated)
if show_world:
# register a handler to plot the world if show_world is active
env.world_updated.register(self.on_world_updated)
# connect the channels with the observed input bits
session.env_token_updated.register(self.on_env_token_updated)
session.learner_token_updated.register(self.on_learner_token_updated)
del self.info['current_task']
def __init__(self,
serializer,
task_scheduler,
scramble=False,
max_reward_per_task=10):
# save parameters into member variables
self._task_scheduler = task_scheduler
self._serializer = serializer
self._max_reward_per_task = max_reward_per_task
# cumulative reward per task
self._reward_per_task = defaultdict(int)
# the event manager is the controller that dispatches
# changes in the environment (like new inputs or state changes)
# to handler functions in the tasks that tell the environment
# how to react
self.event_manager = EventManager()
# intialize member variables
self._current_task = None
self._current_world = None
# we hear to our own output
self._output_channel_listener = InputChannel(serializer)
if scramble:
serializer = ScramblingSerializerWrapper(serializer)
# output channel
self._output_channel = OutputChannel(serializer)
# input channel
self._input_channel = InputChannel(serializer)
# priority of ongoing message
self._output_priority = 0
# reward that is to be given at the learner at the end of the task
self._reward = None
# Current task time
self._task_time = None
# Task deinitialized
self._current_task_deinitialized = False
# Internal logger
self.logger = logging.getLogger(__name__)
# signals
self.world_updated = Observable()
self.task_updated = Observable()
self.reward_given = Observable()
# Register channel observers
self._input_channel.sequence_updated.register(
self._on_input_sequence_updated)
self._input_channel.message_updated.register(
self._on_input_message_updated)
self._output_channel_listener.sequence_updated.register(
self._on_output_sequence_updated)
self._output_channel_listener.message_updated.register(
self._on_output_message_updated)
def __init__(self, serializer):
'''
Takes the serialization protocol
'''
self._serializer = serializer
self._input_channel = InputChannel(serializer)
self._output_channel = OutputChannel(serializer)
self._input_channel.message_updated.register(self.on_message)
self.logger = logging.getLogger(__name__)
self.speaking = False
def __init__(self, serializer, byte_mode):
'''
Takes the serialization protocol
'''
self._serializer = serializer
if byte_mode:
self._input_channel = ByteInputChannel(serializer)
self._output_channel = ByteOutputChannel(serializer)
else:
self._input_channel = InputChannel(serializer)
self._output_channel = OutputChannel(serializer)
self.logger = logging.getLogger(__name__)
self.speaking = False
