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* Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 * Reformatting * Fixing tests * Move atari-py install conditional to req.txt * migrate to new ale install method * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 Move atari-py install conditional to req.txt migrate to new ale install method Make parametric_actions_cartpole return float32 actions/obs Adding type conversions if obs/actions don't match space Add utils to make elements match gym space dtypes Co-authored-by: Jun Gong <jungong@anyscale.com> Co-authored-by: sven1977 <svenmika1977@gmail.com>
74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
import gym
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from typing import Type
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from ray.rllib.utils.annotations import override
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class ActionTransform(gym.ActionWrapper):
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def __init__(self, env, low, high):
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super().__init__(env)
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self._low = low
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self._high = high
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self.action_space = type(env.action_space)(self._low, self._high,
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env.action_space.shape,
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env.action_space.dtype)
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def action(self, action):
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return (action - self._low) / (self._high - self._low) * (
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self.env.action_space.high -
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self.env.action_space.low) + self.env.action_space.low
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def transform_action_space(env_name_or_creator) -> Type[gym.Env]:
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"""Wrapper for gym.Envs to have their action space transformed.
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Args:
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env_name_or_creator (Union[str, Callable[]]: String specifier or
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env_maker function.
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Returns:
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New TransformedActionSpaceEnv class
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to be used as env. The constructor takes a config dict with `_low`
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and `_high` keys specifying the new action range
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(default -1.0 to 1.0). The reset of the config dict will be
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passed on to the underlying/wrapped env's constructor.
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Examples:
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>>> # By gym string:
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>>> pendulum_300_to_500_cls = transform_action_space("Pendulum-v1")
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>>> # Create a transformed pendulum env.
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>>> pendulum_300_to_500 = pendulum_300_to_500_cls({"_low": -15.0})
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>>> pendulum_300_to_500.action_space
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... gym.spaces.Box(-15.0, 1.0, (1, ), "float32")
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"""
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class TransformedActionSpaceEnv(gym.Env):
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"""PendulumEnv w/ an action space of range 300.0 to 500.0."""
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def __init__(self, config):
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self._low = config.pop("low", -1.0)
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self._high = config.pop("high", 1.0)
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if isinstance(env_name_or_creator, str):
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self.env = gym.make(env_name_or_creator)
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else:
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self.env = env_name_or_creator(config)
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self.env = ActionTransform(self.env, self._low, self._high)
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self.observation_space = self.env.observation_space
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self.action_space = self.env.action_space
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@override(gym.Env)
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def reset(self):
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return self.env.reset()
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@override(gym.Env)
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def step(self, actions):
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return self.env.step(actions)
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@override(gym.Env)
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def render(self, mode=None):
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return self.env.render(mode)
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return TransformedActionSpaceEnv
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TransformedActionPendulum = transform_action_space("Pendulum-v1")
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