mirror of
https://github.com/vale981/ray
synced 2025-03-08 11:31:40 -05:00
b463d9e5c7
* Initializing A3C code * Modifications for Ray usage * cleanup * removing universe dependency * fixes (not yet working * hack * documentation * Cleanup * Preliminary Portion Make sure to change when merging * RL part * Cleaning up Driver and Worker code * Updating driver code * instructions... * fixed * Minor changes. * Fixing cmake issues * ray instruction * updating port to new universe * Fix for env.configure * redundant commands * Revert scipy.misc -> cv2 and raise exception for wrong gym version.
93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import gym
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import weakref
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import vectorized.vectorize_core as core
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class Vectorize(gym.Wrapper):
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"""
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Given an unvectorized environment (where, e.g., the output of .step() is an observation
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rather than a list of observations), turn it into a vectorized environment with a batch of size
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1.
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"""
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metadata = {'runtime.vectorized': True}
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def __init__(self, env):
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super(Vectorize, self).__init__(env)
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assert not env.metadata.get('runtime.vectorized')
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assert self.metadata.get('runtime.vectorized')
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self.n = 1
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def _reset(self):
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observation = self.env.reset()
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return [observation]
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def _step(self, action):
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observation, reward, done, info = self.env.step(action[0])
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return [observation], [reward], [done], {'n': [info]}
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def _seed(self, seed):
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return [self.env.seed(seed[0])]
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class Unvectorize(core.Wrapper):
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"""
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Take a vectorized environment with a batch of size 1 and turn it into an unvectorized environment.
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"""
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autovectorize = False
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metadata = {'runtime.vectorized': False}
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def _configure(self, **kwargs):
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super(Unvectorize, self)._configure(**kwargs)
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if self.n != 1:
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raise Exception('Can only disable vectorization with n=1, not n={}'.format(self.n))
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def _reset(self):
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observation_n = self.env.reset()
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return observation_n[0]
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def _step(self, action):
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action_n = [action]
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observation_n, reward_n, done_n, info = self.env.step(action_n)
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return observation_n[0], reward_n[0], done_n[0], info['n'][0]
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def _seed(self, seed):
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return self.env.seed([seed])[0]
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class WeakUnvectorize(Unvectorize):
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def __init__(self, env, i):
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self._env_ref = weakref.ref(env)
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super(WeakUnvectorize, self).__init__(env)
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# WeakUnvectorize won't get configure called on it
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self.i = i
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def _check_for_duplicate_wrappers(self):
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pass # Disable this check because we need to wrap vectorized envs in multiple unvectorize wrappers
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@property
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def env(self):
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# Called upon instantiation
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if not hasattr(self, '_env_ref'):
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return
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env = self._env_ref()
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if env is None:
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raise Exception("env has been garbage collected. To keep using WeakUnvectorize, you must keep around a reference to the env object. (HINT: try assigning the env to a variable in your code.)")
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return env
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@env.setter
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def env(self, value):
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# We'll maintain our own weakref, thank you very much.
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pass
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def _seed(self, seed):
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# We handle the seeding ourselves in the vectorized Monitor
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return [seed]
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def close(self):
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# Don't want to close through this wrapper
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pass
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