File size: 1,885 Bytes
5960497 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | from baselines.common.input import observation_input
from baselines.common.tf_util import adjust_shape
# ================================================================
# Placeholders
# ================================================================
class TfInput(object):
def __init__(self, name="(unnamed)"):
"""Generalized Tensorflow placeholder. The main differences are:
- possibly uses multiple placeholders internally and returns multiple values
- can apply light postprocessing to the value feed to placeholder.
"""
self.name = name
def get(self):
"""Return the tf variable(s) representing the possibly postprocessed value
of placeholder(s).
"""
raise NotImplementedError
def make_feed_dict(self, data):
"""Given data input it to the placeholder(s)."""
raise NotImplementedError
class PlaceholderTfInput(TfInput):
def __init__(self, placeholder):
"""Wrapper for regular tensorflow placeholder."""
super().__init__(placeholder.name)
self._placeholder = placeholder
def get(self):
return self._placeholder
def make_feed_dict(self, data):
return {self._placeholder: adjust_shape(self._placeholder, data)}
class ObservationInput(PlaceholderTfInput):
def __init__(self, observation_space, name=None):
"""Creates an input placeholder tailored to a specific observation space
Parameters
----------
observation_space:
observation space of the environment. Should be one of the gym.spaces types
name: str
tensorflow name of the underlying placeholder
"""
inpt, self.processed_inpt = observation_input(observation_space, name=name)
super().__init__(inpt)
def get(self):
return self.processed_inpt
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