PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems

Overview

PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems

ci_workflow codeql_workflow

Authors: David Biagioni, Xiangyu Zhang, Dylan Wald, Deepthi Vaidhynathan, Rhoit Chintala, Jennifer King, Ahmed S. Zamzam

Corresponding author: David Biagioni

All authors are with the National Renewable Energy Laboratory (NREL).

Description

PowerGridworld provides users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environments that readily integrate with existing training frameworks for reinforcement learning (RL). Although many frameworks exist for training multi-agent RL (MARL) policies, none can rapidly prototype and develop the environments themselves, especially in the context of heterogeneous (composite, multidevice) power systems where power flow solutions are required to define grid-level variables and costs. PowerGridworld is an opensource software package that helps to fill this gap. To highlight PowerGridworld’s key features, we include two case studies and demonstrate learning MARL policies using both OpenAI’s multi-agent deep deterministic policy gradient (MADDPG) and RLLib’s proximal policy optimization (PPO) algorithms. In both cases, at least some subset of agents incorporates elements of the power flow solution at each time step as part of their reward (negative cost) structures.

Please refer to our preprint on arXiv for more details. Data and run scripts used to generate figures in the preprint are available in the paper directory.

Basic installation instructions

Env setup:

conda create -n gridworld python=3.8 -y
conda activate gridworld

git clone [email protected]:NREL/PowerGridworld.git
cd PowerGridWorld
pip install -e .
pip install -r requirements.txt

Run the pytests to sanity check:

pytest tests/
pytests --nbmake examples/envs

Examples

Examples of running various environments and MARL training algorithms can be found in examples.

Funding Acknowledgement

This work was authored by the National Renewable Energy Laboratory (NREL), operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. This work was supported by the Laboratory Directed Research and Development (LDRD) Program at NREL.

Citation

If citing this work, please use the following:

@article{biagioni2021powergridworld,
  title={PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems},
  author={Biagioni, David and Zhang, Xiangyu and Wald, Dylan and Vaidhynathan, Deepthi and Chintala, Rohit and King, Jennifer and Zamzam, Ahmed S},
  journal={arXiv preprint arXiv:2111.05969},
  year={2021}
}
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Comments
  • Bump tensorflow from 1.8.0 to 2.5.2 in /examples/marl/openai

    Bump tensorflow from 1.8.0 to 2.5.2 in /examples/marl/openai

    Bumps tensorflow from 1.8.0 to 2.5.2.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.5.2

    Release 2.5.2

    This release introduces several vulnerability fixes:

    • Fixes a code injection issue in saved_model_cli (CVE-2021-41228)
    • Fixes a vulnerability due to use of uninitialized value in Tensorflow (CVE-2021-41225)
    • Fixes a heap OOB in FusedBatchNorm kernels (CVE-2021-41223)
    • Fixes an arbitrary memory read in ImmutableConst (CVE-2021-41227)
    • Fixes a heap OOB in SparseBinCount (CVE-2021-41226)
    • Fixes a heap OOB in SparseFillEmptyRows (CVE-2021-41224)
    • Fixes a segfault due to negative splits in SplitV (CVE-2021-41222)
    • Fixes segfaults and vulnerabilities caused by accesses to invalid memory during shape inference in Cudnn* ops (CVE-2021-41221)
    • Fixes a null pointer exception when Exit node is not preceded by Enter op (CVE-2021-41217)
    • Fixes an integer division by 0 in tf.raw_ops.AllToAll (CVE-2021-41218)
    • Fixes an undefined behavior via nullptr reference binding in sparse matrix multiplication (CVE-2021-41219)
    • Fixes a heap buffer overflow in Transpose (CVE-2021-41216)
    • Prevents deadlocks arising from mutually recursive tf.function objects (CVE-2021-41213)
    • Fixes a null pointer exception in DeserializeSparse (CVE-2021-41215)
    • Fixes an undefined behavior arising from reference binding to nullptr in tf.ragged.cross (CVE-2021-41214)
    • Fixes a heap OOB read in tf.ragged.cross (CVE-2021-41212)
    • Fixes a heap OOB read in all tf.raw_ops.QuantizeAndDequantizeV* ops (CVE-2021-41205)
    • Fixes an FPE in ParallelConcat (CVE-2021-41207)
    • Fixes FPE issues in convolutions with zero size filters (CVE-2021-41209)
    • Fixes a heap OOB read in tf.raw_ops.SparseCountSparseOutput (CVE-2021-41210)
    • Fixes vulnerabilities caused by incomplete validation in boosted trees code (CVE-2021-41208)
    • Fixes vulnerabilities caused by incomplete validation of shapes in multiple TF ops (CVE-2021-41206)
    • Fixes a segfault produced while copying constant resource tensor (CVE-2021-41204)
    • Fixes a vulnerability caused by unitialized access in EinsumHelper::ParseEquation (CVE-2021-41201)
    • Fixes several vulnerabilities and segfaults caused by missing validation during checkpoint loading (CVE-2021-41203)
    • Fixes an overflow producing a crash in tf.range (CVE-2021-41202)
    • Fixes an overflow producing a crash in tf.image.resize when size is large (CVE-2021-41199)
    • Fixes an overflow producing a crash in tf.tile when tiling tensor is large (CVE-2021-41198)
    • Fixes a vulnerability produced due to incomplete validation in tf.summary.create_file_writer (CVE-2021-41200)
    • Fixes multiple crashes due to overflow and CHECK-fail in ops with large tensor shapes (CVE-2021-41197)
    • Fixes a crash in max_pool3d when size argument is 0 or negative (CVE-2021-41196)
    • Fixes a crash in tf.math.segment_* operations (CVE-2021-41195)
    • Updates curl to 7.78.0 to handle CVE-2021-22922, CVE-2021-22923, CVE-2021-22924, CVE-2021-22925, and CVE-2021-22926.

    TensorFlow 2.5.1

    Release 2.5.1

    This release introduces several vulnerability fixes:

    • Fixes a heap out of bounds access in sparse reduction operations (CVE-2021-37635)
    • Fixes a floating point exception in SparseDenseCwiseDiv (CVE-2021-37636)
    • Fixes a null pointer dereference in CompressElement (CVE-2021-37637)
    • Fixes a null pointer dereference in RaggedTensorToTensor (CVE-2021-37638)
    • Fixes a null pointer dereference and a heap OOB read arising from operations restoring tensors (CVE-2021-37639)
    • Fixes an integer division by 0 in sparse reshaping (CVE-2021-37640)

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.5.2

    This release introduces several vulnerability fixes:

    ... (truncated)

    Commits
    • 957590e Merge pull request #52873 from tensorflow-jenkins/relnotes-2.5.2-20787
    • 2e1d16d Update RELEASE.md
    • 2fa6dd9 Merge pull request #52877 from tensorflow-jenkins/version-numbers-2.5.2-192
    • 4807489 Merge pull request #52881 from tensorflow/fix-build-1-on-r2.5
    • d398bdf Disable failing test
    • 857ad5e Merge pull request #52878 from tensorflow/fix-build-1-on-r2.5
    • 6c2a215 Disable failing test
    • f5c57d4 Update version numbers to 2.5.2
    • e51f949 Insert release notes place-fill
    • 2620d2c Merge pull request #52863 from tensorflow/fix-build-3-on-r2.5
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    Verified that rllib results on Eagle are qualitatively the same as reported in paper. Updated some documentation. Added notebook tests just sanity checking that no errors are raised when run.

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