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026bf01071
* 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>
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
import numpy as np
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import gym
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from ray.rllib.utils.annotations import PublicAPI
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@PublicAPI
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class Simplex(gym.Space):
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"""Represents a d - 1 dimensional Simplex in R^d.
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That is, all coordinates are in [0, 1] and sum to 1.
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The dimension d of the simplex is assumed to be shape[-1].
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Additionally one can specify the underlying distribution of
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the simplex as a Dirichlet distribution by providing concentration
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parameters. By default, sampling is uniform, i.e. concentration is
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all 1s.
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Example usage:
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self.action_space = spaces.Simplex(shape=(3, 4))
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--> 3 independent 4d Dirichlet with uniform concentration
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"""
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def __init__(self, shape, concentration=None, dtype=np.float32):
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assert type(shape) in [tuple, list]
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super().__init__(shape, dtype)
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self.dim = self.shape[-1]
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if concentration is not None:
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assert concentration.shape == shape[:-1]
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else:
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self.concentration = [1] * self.dim
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def sample(self):
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return np.random.dirichlet(
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self.concentration, size=self.shape[:-1]).astype(self.dtype)
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def contains(self, x):
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return x.shape == self.shape and np.allclose(
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np.sum(x, axis=-1), np.ones_like(x[..., 0]))
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def to_jsonable(self, sample_n):
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return np.array(sample_n).tolist()
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def from_jsonable(self, sample_n):
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return [np.asarray(sample) for sample in sample_n]
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def __repr__(self):
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return "Simplex({}; {})".format(self.shape, self.concentration)
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def __eq__(self, other):
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return np.allclose(self.concentration,
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other.concentration) and self.shape == other.shape
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