def __init__(self,
env,
act_null_value=0,
obs_null_value=0,
force_float32=True):
super().__init__(env)
o = self.env.reset()
o, r, d, info = self.env.step(self.env.action_space.sample())
env_ = self.env
time_limit = isinstance(self.env, TimeLimit)
while not time_limit and hasattr(env_, "env"):
env_ = env_.env
time_limit = isinstance(self.env, TimeLimit)
if time_limit:
info["timeout"] = False # gym's TimeLimit.truncated invalid name.
self._time_limit = time_limit
self.action_space = GymSpaceWrapper(
space=self.env.action_space,
name="act",
null_value=act_null_value,
force_float32=force_float32,
)
self.observation_space = GymSpaceWrapper(
space=self.env.observation_space,
name="obs",
null_value=obs_null_value,
force_float32=force_float32,
)
self._info_schemas = {}
self._build_info_schemas(info)
class GymEnvWrapper(Wrapper):
"""Gym-style wrapper for converting the Openai Gym interface to the
rlpyt interface. Action and observation spaces are wrapped by rlpyt's
``GymSpaceWrapper``.
Output `env_info` is automatically converted from a dictionary to a
corresponding namedtuple, which the rlpyt sampler expects. For this to
work, every key that might appear in the gym environments `env_info` at
any step must appear at the first step after a reset, as the `env_info`
entries will have sampler memory pre-allocated for them (so they also
cannot change dtype or shape). (see `EnvInfoWrapper`, `build_info_schemas`,
and `info_to_nt` in file or more help/details)
Warning:
Unrecognized keys in `env_info` appearing later during use will be
silently ignored.
This wrapper looks for gym's ``TimeLimit`` env wrapper to
see whether to add the field ``timeout`` to env info.
"""
def __init__(self,
env,
act_null_value=0,
obs_null_value=0,
force_float32=True):
super().__init__(env)
o = self.env.reset()
o, r, d, info = self.env.step(self.env.action_space.sample())
env_ = self.env
time_limit = isinstance(self.env, TimeLimit)
while not time_limit and hasattr(env_, "env"):
env_ = env_.env
time_limit = isinstance(self.env, TimeLimit)
if time_limit:
info["timeout"] = False # gym's TimeLimit.truncated invalid name.
self._time_limit = time_limit
self.action_space = GymSpaceWrapper(
space=self.env.action_space,
name="act",
null_value=act_null_value,
force_float32=force_float32,
)
self.observation_space = GymSpaceWrapper(
space=self.env.observation_space,
name="obs",
null_value=obs_null_value,
force_float32=force_float32,
)
self._info_schemas = {}
self._build_info_schemas(info)
def _build_info_schemas(self, info, name="info"):
ntc = self._info_schemas.get(name)
if ntc is None:
self._info_schemas[name] = NamedTupleSchema(
name, list(info.keys()))
elif not (isinstance(ntc, NamedTupleSchema)
and sorted(ntc._fields) == sorted(list(info.keys()))):
raise ValueError(f"Name clash in schema index: {name}.")
for k, v in info.items():
if isinstance(v, dict):
self._build_info_schemas(v, "_".join([name, k]))
def step(self, action):
"""Reverts the action from rlpyt format to gym format (i.e. if composite-to-
dictionary spaces), steps the gym environment, converts the observation
from gym to rlpyt format (i.e. if dict-to-composite), and converts the
env_info from dictionary into namedtuple."""
a = self.action_space.revert(action)
o, r, d, info = self.env.step(a)
obs = self.observation_space.convert(o)
if self._time_limit:
if "TimeLimit.truncated" in info:
info["timeout"] = info.pop("TimeLimit.truncated")
else:
info["timeout"] = False
info = info_to_nt(info, self._info_schemas)
return EnvStep(obs, r, d, info)
def reset(self):
"""Returns converted observation from gym env reset."""
return self.observation_space.convert(self.env.reset())
@property
def spaces(self):
"""Returns the rlpyt spaces for the wrapped env."""
return EnvSpaces(
observation=self.observation_space,
action=self.action_space,
)