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Tensor utilities, reinforcement learning, and more!

Project description


Tensor utilities, reinforcement learning, and more! Designed to make research easier with low-level abstractions for common operations.


For the top-level utilities, import like so:

import pyoneer as pynr

For the larger sub-modules, such as reinforcement learning, we recommend:

import pyoneer.rl as pyrl
loss_fn = pyrl.losses.PolicyGradient(...)

In general, the Pyoneer API tries to adhere to the TensorFlow 2.0 API.



Activations (pynr.activations)

  • pynr.activations.swish

Debugging (pynr.debugging)

  • pynr.debugging.Stopwatch

Distributions (pynr.distributions)

  • pynr.distributions.MultiCategorical

Initializers (pynr.initializers)

  • pynr.initializers.SoftplusInverse

Layers (pynr.layers)

  • pynr.layers.Swish
  • pynr.layers.OneHotEncoder
  • pynr.layers.AngleEncoder
  • pynr.layers.Nest

Tensor Manipulation (pynr.manip)

  • pynr.manip.flatten
  • pynr.manip.batched_index
  • pynr.manip.pad_or_truncate
  • pynr.manip.shift

Math (pynr.math)

  • pynr.math.to_radians
  • pynr.math.to_degrees
  • pynr.math.to_cartesian
  • pynr.math.to_polar
  • pynr.math.RADIANS_TO_DEGREES
  • pynr.math.DEGREES_TO_RADIANS
  • pynr.math.isclose
  • pynr.math.safe_divide
  • pynr.math.rescale
  • pynr.math.normalize
  • pynr.math.denormalize

Metrics (pynr.metrics)

  • pynr.metrics.mape
  • pynr.metrics.smape
  • pynr.metrics.MAPE
  • pynr.metrics.SMAPE

Moments (pynr.moments)

  • pynr.moments.range_moments
  • pynr.moments.StaticMoments
  • pynr.moments.StreamingMoments
  • pynr.moments.ExponentialMovingMoments

Learning Rate Schedules (pynr.schedules)

  • pynr.schedules.CyclicSchedule

Reinforcement Learning (pynr.rl)

Utilities for reinforcement learning.

Losses (pynr.rl.losses)

  • pynr.rl.losses.policy_gradient
  • pynr.rl.losses.policy_entropy
  • pynr.rl.losses.clipped_policy_gradient
  • pynr.rl.losses.PolicyGradient
  • pynr.rl.losses.PolicyEntropy
  • pynr.rl.losses.ClippedPolicyGradient

Targets (pynr.rl.targets)

  • pynr.rl.targets.DiscountedReturns
  • pynr.rl.targets.GeneralizedAdvantages

Strategies (pynr.rl.strategies)

  • pynr.rl.strategies.EpsilonGreedy
  • pynr.rl.strategies.Mode
  • pynr.rl.strategies.Sample

Wrappers (pynr.rl.wrappers)

  • pynr.rl.wrappers.ObservationCoordinates
  • pynr.rl.wrappers.ObservationNormalization
  • pynr.rl.wrappers.Batch
  • pynr.rl.wrappers.Process


There are a few options for installation:

  1. (Recommended) Install with pipenv:

     pipenv install fomoro-pyoneer
  2. Install locally for development with pipenv:

     git clone
     cd pyoneer
     pipenv install
     pipenv shell


There are a few options for testing:

  1. Run all tests:

     python -m unittest discover -bfp '*'
  2. Run specific tests:

     python -m pyoneer.math.logical_ops_test


File an issue following the ISSUE_TEMPLATE. If the issue discussion warrants implementation, then submit a pull request from a branch describing the feature. This will eventually get merged into master after a few rounds of code review.

Project details

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