A bare-bones Python library for quality diversity optimization.

Overview

pyribs

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A bare-bones Python library for quality diversity optimization. pyribs is the official implementation of the Covariance Matrix Adaptation MAP-Elites (CMA-ME) algorithm and implements the Rapid Illumination of Behavior Space (RIBS) redesign of MAP-Elites detailed in the paper Covariance Matrix Adapation for the Rapid Illumination of Behavior Space.

Overview

Types of Optimization

Quality diversity (QD) optimization is a subfield of optimization where solutions generated cover every point in a behavior space while simultaneously maximizing (or minimizing) a single objective. QD algorithms within the MAP-Elites family of QD algorithms produce heatmaps (archives) as output where each cell contains the best discovered representative of a region in behavior space.

While many QD libraries exist, this particular library aims to be the QD analog to the pycma library (a single objective optimization library). In contrast to other QD libraries, this library is "bare-bones," meaning pyribs (like pycma) focuses solely on optimizing fixed-dimensional continuous domains. Focusing solely on this one commonly-occurring problem allows us to optimize the library for performance as well as simplicity of use. For applications of QD on discrete domains, we recommend using qdpy or sferes.

A user of pyribs selects three components that meet the needs of their application:

  • An Archive saves the best representatives generated within behavior space.
  • Emitters control how new candidate solutions are generated and affect if the algorithm prioritizes quality or diversity.
  • An Optimizer joins the Archive and Emitters together and acts as a scheduling algorithm for emitters. The Optimizer provides an interface for requesting new candidate solutions and telling the algorithm how candidates performed.

Citation

If you use pyribs in your research, please cite it as follows. Note that you will need to include the hyperref package in order to use the \url command.

@misc{pyribs,
  title = {pyribs: A bare-bones Python library for quality diversity
           optimization},
  author = {Bryon Tjanaka and Matthew C. Fontaine and Yulun Zhang and
            Sam Sommerer and Nathan Dennler and Stefanos Nikolaidis},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/icaros-usc/pyribs}},
}

If you use the CMA-ME algorithm, please also cite Fontaine 2020.

@inproceedings{10.1145/3377930.3390232,
  author = {Fontaine, Matthew C. and Togelius, Julian and Nikolaidis, Stefanos and Hoover, Amy K.},
  title = {Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space},
  year = {2020},
  isbn = {9781450371285},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3377930.3390232},
  doi = {10.1145/3377930.3390232},
  booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation Conference},
  pages = {94–102},
  numpages = {9},
  location = {Canc\'{u}n, Mexico},
  series = {GECCO '20}
}

Usage

Here we show an example application of CMA-ME in pyribs. To initialize the algorithm, we first create:

  • A 2D GridArchive where each dimension contains 20 bins across the range [-1, 1].
  • An ImprovementEmitter, which starts from the search point 0 in 10 dimensional space and a Gaussian sampling distribution with standard deviation 0.1.
  • An Optimizer that combines the archive and emitter together.

After initializing the components, we optimize (pyribs maximizes) the negative 10-D Sphere function for 1000 iterations. Users of pycma will be familiar with the ask-tell interface (which pyribs adopted). First, the user must ask the optimizer for new candidate solutions. After evaluating the solution, they tell the optimizer the objective value and behavior characteristics (BCs) of each candidate solution. The algorithm then populates the archive and makes decisions on where to sample solutions next. Our toy example uses the first two parameters of the search space as BCs.

import numpy as np

from ribs.archives import GridArchive
from ribs.emitters import ImprovementEmitter
from ribs.optimizers import Optimizer

archive = GridArchive([20, 20], [(-1, 1), (-1, 1)])
emitters = [ImprovementEmitter(archive, [0.0] * 10, 0.1)]
optimizer = Optimizer(archive, emitters)

for itr in range(1000):
    solutions = optimizer.ask()

    objectives = -np.sum(np.square(solutions), axis=1)
    bcs = solutions[:, :2]

    optimizer.tell(objectives, bcs)

To visualize this archive with matplotlib, we then use the grid_archive_heatmap function from ribs.visualize.

import matplotlib.pyplot as plt
from ribs.visualize import grid_archive_heatmap

grid_archive_heatmap(archive)
plt.show()

Sphere heatmap

For more information, refer to the documentation.

Installation

pyribs supports Python 3.6-3.9. Earlier Python versions may work but are not officially supported.

To install from PyPI, run

pip install ribs

This command only installs dependencies for the core of pyribs. To install support tools like ribs.visualize, run

pip install ribs[all]

Equivalently, you can install the base version (equivalent to ribs) from Conda with

conda install -c conda-forge pyribs-base

The full version (equivalent to ribs[all]) can be installed with

conda install -c conda-forge pyribs

To test your installation, import it and print the version with:

python -c "import ribs; print(ribs.__version__)"

You should see a version number like 0.2.0 in the output.

From Source

To install a version from source, clone the repo

git clone https://github.com/icaros-usc/pyribs

Then cd into it

cd pyribs

And run

pip install -e .[all]

Documentation

See here for the documentation: https://docs.pyribs.org

To serve the documentation locally, clone the repo and install the development requirements with

pip install -e .[dev]

Then run

make servedocs

This will open a window in your browser with the documentation automatically loaded. Furthermore, every time you make changes to the documentation, the preview will also reload.

Contributors

pyribs is developed and maintained by the ICAROS Lab at USC.

We thank Amy K. Hoover and Julian Togelius for their contributions deriving the CMA-ME algorithm.

License

pyribs is released under the MIT License.

Credits

The pyribs package was initially created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

Comments
  • [BUG]  std::__1::system_error: condition_variable wait failed: Invalid argument

    [BUG] std::__1::system_error: condition_variable wait failed: Invalid argument

    • pyribs version: 0.4.0
    • Python version: 3.9
    • Operating System: Mac (Big Sur)

    Description

    I run pyribs code from poetry and python3.9. However, when I run the code for 12 ~ 36 hours, sometimes the code crushed...

    The error message is here.

    Application Specific Information:
    terminating with uncaught exception of type std::__1::system_error: condition_variable wait failed: Invalid argument
    abort() called
    

    I think this problem is arised from llvm or llvm-lite, because this error is due to thread ordering problem (maybe).

    Steps to Reproduce

    This command runs learning with pytorch and pyribs.

    nohup python -m mario_pytorch.cli learn-pyribs coin_enemy.yaml coin_enemy.yaml coin_enemy.yaml &
    

    Error traceback is here.

    Trace back
    Process:               Python [55836]
    Path:                  /usr/local/Cellar/[email protected]/3.9.7/Frameworks/Python.framework/Versions/3.9/Resources/Python.app/Contents/MacOS/Python
    Identifier:            org.python.python
    Version:               3.9.7 (3.9.7)
    Code Type:             X86-64 (Native)
    Parent Process:        zsh [14421]
    Responsible:           alacritty [1167]
    User ID:               501
    
    Date/Time:             2021-09-28 22:30:22.093 +0900
    OS Version:            macOS 11.6 (20G165)
    Report Version:        12
    Anonymous UUID:        795A460E-4BBC-7C3A-D5D0-447C26513803
    
    Sleep/Wake UUID:       FDBB4B4C-C0BA-41B9-A29C-828E3B109322
    
    Time Awake Since Boot: 480000 seconds
    Time Since Wake:       300000 seconds
    
    System Integrity Protection: enabled
    
    Crashed Thread:        8
    
    Exception Type:        EXC_CRASH (SIGABRT)
    Exception Codes:       0x0000000000000000, 0x0000000000000000
    Exception Note:        EXC_CORPSE_NOTIFY
    
    Application Specific Information:
    terminating with uncaught exception of type std::__1::system_error: condition_variable wait failed: Invalid argument
    abort() called
    
    Thread 0:: Dispatch queue: com.apple.main-thread
    0   libsystem_kernel.dylib        	0x00007fff203774ca __psynch_mutexwait + 10
    1   libsystem_pthread.dylib       	0x00007fff203a82ab _pthread_mutex_firstfit_lock_wait + 76
    2   libsystem_pthread.dylib       	0x00007fff203a6192 _pthread_mutex_firstfit_lock_slow + 204
    3   libc++.1.dylib                	0x00007fff203413d9 std::__1::mutex::lock() + 9
    4   libc10.dylib                  	0x00000001265fff76 c10::ThreadPool::run(std::__1::function<void ()>) + 38
    5   libtorch_cpu.dylib            	0x00000001144beb29 at::internal::_parallel_run(long long, long long, long long, std::__1::function<void (long long, long long, unsigned long)> const&) + 969
    6   libtorch_cpu.dylib            	0x00000001182bf0e9 void at::vml::(anonymous namespace)::vsqrt<float>(float*, float const*, long long) + 169
    7   libtorch_cpu.dylib            	0x00000001182beca2 void c10::function_ref<void (char**, long long const*, long long, long long)>::callback_fn<auto at::TensorIteratorBase::loop_2d_from_1d<at::native::AVX2::sqrt_kernel(at::TensorIteratorBase&)::$_50::operator()() const::'lambda2'()::operator()() const::'lambda'(char**, long long const*, long long)>(at::native::AVX2::sqrt_kernel(at::TensorIteratorBase&)::$_50::operator()() const::'lambda2'()::operator()() const::'lambda'(char**, long long const*, long long) const&)::'lambda'(char**, long long const*, long long, long long)>(long, char**, long long const*, long long, long long) + 274
    8   libtorch_cpu.dylib            	0x00000001144db370 at::TensorIteratorBase::serial_for_each(c10::function_ref<void (char**, long long const*, long long, long long)>, at::Range) const + 560
    9   libtorch_cpu.dylib            	0x00000001181cf213 at::native::AVX2::sqrt_kernel(at::TensorIteratorBase&) + 291
    10  libtorch_cpu.dylib            	0x00000001151076cc c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor (at::Tensor const&), &(at::(anonymous namespace)::wrapper_sqrt(at::Tensor const&))>, at::Tensor, c10::guts::typelist::typelist<at::Tensor const&> >, at::Tensor (at::Tensor const&)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&) + 108
    11  libtorch_cpu.dylib            	0x0000000114f49fc2 at::redispatch::sqrt(c10::DispatchKeySet, at::Tensor const&) + 130
    12  libtorch_cpu.dylib            	0x0000000116d22010 c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor (c10::DispatchKeySet, at::Tensor const&), &(torch::autograd::VariableType::(anonymous namespace)::sqrt(c10::DispatchKeySet, at::Tensor const&))>, at::Tensor, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor const&> >, at::Tensor (c10::DispatchKeySet, at::Tensor const&)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&) + 448
    13  libtorch_cpu.dylib            	0x000000011544b10d at::Tensor::sqrt() const + 221
    14  libtorch_python.dylib         	0x0000000112d965c8 torch::autograd::THPVariable_sqrt(_object*, _object*) + 232
    15  org.python.python             	0x0000000104c76315 method_vectorcall_NOARGS + 99
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    17  org.python.python             	0x0000000104d5fc85 _PyEval_EvalFrameDefault + 42484
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    21  org.python.python             	0x0000000104d5fdfd _PyEval_EvalFrameDefault + 42860
    22  org.python.python             	0x0000000104d545f6 _PyEval_EvalCode + 406
    23  org.python.python             	0x0000000104c6aadc _PyFunction_Vectorcall + 376
    24  org.python.python             	0x0000000104d5ff4c _PyEval_EvalFrameDefault + 43195
    25  org.python.python             	0x0000000104d545f6 _PyEval_EvalCode + 406
    26  org.python.python             	0x0000000104c6aadc _PyFunction_Vectorcall + 376
    27  org.python.python             	0x0000000104d5ff4c _PyEval_EvalFrameDefault + 43195
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    34  org.python.python             	0x0000000104d5fc85 _PyEval_EvalFrameDefault + 42484
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    36  org.python.python             	0x0000000104d62a0e call_function + 164
    37  org.python.python             	0x0000000104d5fc85 _PyEval_EvalFrameDefault + 42484
    38  org.python.python             	0x0000000104d545f6 _PyEval_EvalCode + 406
    39  org.python.python             	0x0000000104c6aadc _PyFunction_Vectorcall + 376
    40  org.python.python             	0x0000000104d62a0e call_function + 164
    41  org.python.python             	0x0000000104d5fd4e _PyEval_EvalFrameDefault + 42685
    42  org.python.python             	0x0000000104c6aa24 _PyFunction_Vectorcall + 192
    43  org.python.python             	0x0000000104d62a0e call_function + 164
    44  org.python.python             	0x0000000104d5fd4e _PyEval_EvalFrameDefault + 42685
    45  org.python.python             	0x0000000104c6aa24 _PyFunction_Vectorcall + 192
    46  org.python.python             	0x0000000104d62a0e call_function + 164
    47  org.python.python             	0x0000000104d5fd4e _PyEval_EvalFrameDefault + 42685
    48  org.python.python             	0x0000000104c6aa24 _PyFunction_Vectorcall + 192
    49  org.python.python             	0x0000000104d62a0e call_function + 164
    50  org.python.python             	0x0000000104d5fd4e _PyEval_EvalFrameDefault + 42685
    51  org.python.python             	0x0000000104d545f6 _PyEval_EvalCode + 406
    52  org.python.python             	0x0000000104c6aadc _PyFunction_Vectorcall + 376
    53  org.python.python             	0x0000000104c6a6c5 PyVectorcall_Call + 164
    54  org.python.python             	0x0000000104d5ff4c _PyEval_EvalFrameDefault + 43195
    55  org.python.python             	0x0000000104d545f6 _PyEval_EvalCode + 406
    56  org.python.python             	0x0000000104c6aadc _PyFunction_Vectorcall + 376
    57  org.python.python             	0x0000000104c6a6c5 PyVectorcall_Call + 164
    58  org.python.python             	0x0000000104d5ff4c _PyEval_EvalFrameDefault + 43195
    59  org.python.python             	0x0000000104d545f6 _PyEval_EvalCode + 406
    60  org.python.python             	0x0000000104c6aadc _PyFunction_Vectorcall + 376
    61  org.python.python             	0x0000000104c6d647 method_vectorcall + 160
    62  org.python.python             	0x0000000104c6a6c5 PyVectorcall_Call + 164
    63  org.python.python             	0x0000000104d5ff4c _PyEval_EvalFrameDefault + 43195
    64  org.python.python             	0x0000000104c6aa24 _PyFunction_Vectorcall + 192
    65  org.python.python             	0x0000000104d62a0e call_function + 164
    66  org.python.python             	0x0000000104d5fc85 _PyEval_EvalFrameDefault + 42484
    67  org.python.python             	0x0000000104d545f6 _PyEval_EvalCode + 406
    68  org.python.python             	0x0000000104c6aadc _PyFunction_Vectorcall + 376
    69  org.python.python             	0x0000000104d62a0e call_function + 164
    70  org.python.python             	0x0000000104d5fc85 _PyEval_EvalFrameDefault + 42484
    71  org.python.python             	0x0000000104d545f6 _PyEval_EvalCode + 406
    72  org.python.python             	0x0000000104c6aadc _PyFunction_Vectorcall + 376
    73  org.python.python             	0x0000000104c6d788 method_vectorcall + 481
    74  org.python.python             	0x0000000104d5ff4c _PyEval_EvalFrameDefault + 43195
    75  org.python.python             	0x0000000104d545f6 _PyEval_EvalCode + 406
    76  org.python.python             	0x0000000104c6aadc _PyFunction_Vectorcall + 376
    77  org.python.python             	0x0000000104c6a0eb _PyObject_FastCallDictTstate + 87
    78  org.python.python             	0x0000000104cd7ee9 slot_tp_call + 187
    79  org.python.python             	0x0000000104c6a851 _PyObject_Call + 125
    80  org.python.python             	0x0000000104d5ff4c _PyEval_EvalFrameDefault + 43195
    81  org.python.python             	0x0000000104d545f6 _PyEval_EvalCode + 406
    82  org.python.python             	0x0000000104c6aadc _PyFunction_Vectorcall + 376
    83  org.python.python             	0x0000000104c6a0eb _PyObject_FastCallDictTstate + 87
    84  org.python.python             	0x0000000104cd7ee9 slot_tp_call + 187
    85  org.python.python             	0x0000000104c69ebe _PyObject_MakeTpCall + 129
    86  org.python.python             	0x0000000104d62a78 call_function + 270
    87  org.python.python             	0x0000000104d5fd4e _PyEval_EvalFrameDefault + 42685
    88  org.python.python             	0x0000000104d545f6 _PyEval_EvalCode + 406
    89  org.python.python             	0x0000000104d4f298 builtin_exec + 389
    90  org.python.python             	0x0000000104cb6069 cfunction_vectorcall_FASTCALL + 95
    91  org.python.python             	0x0000000104d62a0e call_function + 164
    92  org.python.python             	0x0000000104d5fd4e _PyEval_EvalFrameDefault + 42685
    93  org.python.python             	0x0000000104d545f6 _PyEval_EvalCode + 406
    94  org.python.python             	0x0000000104c6aadc _PyFunction_Vectorcall + 376
    95  org.python.python             	0x0000000104d62a0e call_function + 164
    96  org.python.python             	0x0000000104d5fd4e _PyEval_EvalFrameDefault + 42685
    97  org.python.python             	0x0000000104d545f6 _PyEval_EvalCode + 406
    98  org.python.python             	0x0000000104c6aadc _PyFunction_Vectorcall + 376
    99  org.python.python             	0x0000000104dcc046 pymain_run_module + 212
    100 org.python.python             	0x0000000104dcbb83 Py_RunMain + 1013
    101 org.python.python             	0x0000000104dccc16 pymain_main + 35
    102 org.python.python             	0x0000000104dcceec Py_BytesMain + 42
    103 libdyld.dylib                 	0x00007fff203c5f3d start + 1
    
    Thread 1:
    0   libsystem_kernel.dylib        	0x00007fff20377cde __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff203aae49 _pthread_cond_wait + 1298
    2   libopenblas.0.dylib           	0x00000001082a221f blas_thread_server + 207
    3   libsystem_pthread.dylib       	0x00007fff203aa8fc _pthread_start + 224
    4   libsystem_pthread.dylib       	0x00007fff203a6443 thread_start + 15
    
    Thread 2:
    0   libsystem_kernel.dylib        	0x00007fff20377cde __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff203aae49 _pthread_cond_wait + 1298
    2   libopenblas.0.dylib           	0x00000001082a221f blas_thread_server + 207
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    Thread 3:
    0   libsystem_kernel.dylib        	0x00007fff20377cde __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff203aae49 _pthread_cond_wait + 1298
    2   libopenblas.0.dylib           	0x00000001082a221f blas_thread_server + 207
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    Thread 4:
    0   libsystem_kernel.dylib        	0x00007fff20377cde __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff203aae49 _pthread_cond_wait + 1298
    2   org.python.python             	0x0000000104dbcc22 PyThread_acquire_lock_timed + 400
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    11  org.python.python             	0x0000000104d5fc85 _PyEval_EvalFrameDefault + 42484
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    Thread 5:
    0   libsystem_kernel.dylib        	0x00007fff20377cde __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff203aae49 _pthread_cond_wait + 1298
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    Thread 6:
    0   libsystem_kernel.dylib        	0x00007fff20377cde __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff203aae49 _pthread_cond_wait + 1298
    2   libopenblas.0.dylib           	0x0000000139d772eb blas_thread_server + 619
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    Thread 7:
    0   libsystem_kernel.dylib        	0x00007fff20377cde __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff203aae49 _pthread_cond_wait + 1298
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    4   libsystem_pthread.dylib       	0x00007fff203a6443 thread_start + 15
    
    Thread 8 Crashed:
    0   libsystem_kernel.dylib        	0x00007fff2037b92e __pthread_kill + 10
    1   libsystem_pthread.dylib       	0x00007fff203aa5bd pthread_kill + 263
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    5   libobjc.A.dylib               	0x00007fff20258595 _objc_terminate() + 104
    6   libc++abi.dylib               	0x00007fff2036d307 std::__terminate(void (*)()) + 8
    7   libc++abi.dylib               	0x00007fff2036d2a9 std::terminate() + 41
    8   libc10.dylib                  	0x000000012660015d c10::ThreadPool::main_loop(unsigned long) + 237
    9   libc10.dylib                  	0x0000000126600953 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::unique_ptr<std::__1::__thread_struct, std::__1::default_delete<std::__1::__thread_struct> >, c10::ThreadPool::ThreadPool(int, int, std::__1::function<void ()>)::$_0> >(void*) + 67
    10  libsystem_pthread.dylib       	0x00007fff203aa8fc _pthread_start + 224
    11  libsystem_pthread.dylib       	0x00007fff203a6443 thread_start + 15
    
    Thread 9:
    0   libsystem_kernel.dylib        	0x00007fff20377cde __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff203aae49 _pthread_cond_wait + 1298
    2   libc++.1.dylib                	0x00007fff20313d72 std::__1::condition_variable::wait(std::__1::unique_lock<std::__1::mutex>&) + 18
    3   libc10.dylib                  	0x000000012660015d c10::ThreadPool::main_loop(unsigned long) + 237
    4   libc10.dylib                  	0x0000000126600953 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::unique_ptr<std::__1::__thread_struct, std::__1::default_delete<std::__1::__thread_struct> >, c10::ThreadPool::ThreadPool(int, int, std::__1::function<void ()>)::$_0> >(void*) + 67
    5   libsystem_pthread.dylib       	0x00007fff203aa8fc _pthread_start + 224
    6   libsystem_pthread.dylib       	0x00007fff203a6443 thread_start + 15
    
    Thread 10:
    0   libsystem_kernel.dylib        	0x00007fff20377cde __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff203aae49 _pthread_cond_wait + 1298
    2   libc++.1.dylib                	0x00007fff20313d72 std::__1::condition_variable::wait(std::__1::unique_lock<std::__1::mutex>&) + 18
    3   libc10.dylib                  	0x000000012660015d c10::ThreadPool::main_loop(unsigned long) + 237
    4   libc10.dylib                  	0x0000000126600953 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::unique_ptr<std::__1::__thread_struct, std::__1::default_delete<std::__1::__thread_struct> >, c10::ThreadPool::ThreadPool(int, int, std::__1::function<void ()>)::$_0> >(void*) + 67
    5   libsystem_pthread.dylib       	0x00007fff203aa8fc _pthread_start + 224
    6   libsystem_pthread.dylib       	0x00007fff203a6443 thread_start + 15
    
    Thread 11:
    0   libsystem_kernel.dylib        	0x00007fff20377cde __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff203aae49 _pthread_cond_wait + 1298
    2   libc++.1.dylib                	0x00007fff20313d72 std::__1::condition_variable::wait(std::__1::unique_lock<std::__1::mutex>&) + 18
    3   libtorch_cpu.dylib            	0x00000001171c79cb torch::autograd::ReadyQueue::pop() + 75
    4   libtorch_cpu.dylib            	0x00000001171c906c torch::autograd::Engine::thread_main(std::__1::shared_ptr<torch::autograd::GraphTask> const&) + 188
    5   libtorch_cpu.dylib            	0x00000001171c877a torch::autograd::Engine::thread_init(int, std::__1::shared_ptr<torch::autograd::ReadyQueue> const&, bool) + 282
    6   libtorch_python.dylib         	0x000000011346b2b1 torch::autograd::python::PythonEngine::thread_init(int, std::__1::shared_ptr<torch::autograd::ReadyQueue> const&, bool) + 97
    7   libtorch_cpu.dylib            	0x00000001171d76f6 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::unique_ptr<std::__1::__thread_struct, std::__1::default_delete<std::__1::__thread_struct> >, void (torch::autograd::Engine::*)(int, std::__1::shared_ptr<torch::autograd::ReadyQueue> const&, bool), torch::autograd::Engine*, int, std::__1::shared_ptr<torch::autograd::ReadyQueue>, bool> >(void*) + 70
    8   libsystem_pthread.dylib       	0x00007fff203aa8fc _pthread_start + 224
    9   libsystem_pthread.dylib       	0x00007fff203a6443 thread_start + 15
    
    Thread 12:
    0   libsystem_pthread.dylib       	0x00007fff203a6420 start_wqthread + 0
    
    Thread 8 crashed with X86 Thread State (64-bit):
      rax: 0x0000000000000000  rbx: 0x0000700003b6b000  rcx: 0x0000700003b6a748  rdx: 0x0000000000000000
      rdi: 0x0000000000001603  rsi: 0x0000000000000006  rbp: 0x0000700003b6a770  rsp: 0x0000700003b6a748
       r8: 0x0000700003b6a610   r9: 0x0000000000000000  r10: 0x0000000000000000  r11: 0x0000000000000246
      r12: 0x0000000000001603  r13: 0x0000003000000008  r14: 0x0000000000000006  r15: 0x0000000000000016
      rip: 0x00007fff2037b92e  rfl: 0x0000000000000246  cr2: 0x00007fff806d6498
      
    Logical CPU:     0
    Error Code:      0x02000148
    Trap Number:     133
    
    Thread 8 instruction stream not available.
    
    Thread 8 last branch register state not available.
    
    
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    bug 
    opened by ganyariya 10
  • [BUG] Reevaluated agents are not matching archive BCs

    [BUG] Reevaluated agents are not matching archive BCs

    • pyribs version: 0.3.1
    • Python version: 3.7
    • Operating System: Windows
    • Emitter style: Improvement

    Description

    Trying to do: So, I am evolving an agent to play Zelda, and as my behavior characteristics, I am using the average action distribution entropy for the agent during the rollout and the number of steps the agent took to achieve the task (get key and get to the door). As the objective, I am +1 if the agents gets the key, +1 if the agent gets the key to the door, -1 if the agent dies to the single monster, and 0 otherwise. Therefore, an objective score of +2 is "solved".

    What is going wrong: When I extract a solution from the archive at the end of training, e.g: image

    the observed behaviour when I place the agent back into the game does not match the behaviour characterizations that the map says this network weights should achieve. To ensure that I wasn't losing precision due to saving the archive, I have been saving the dataframes in a pkl file. However, that is not a concern here since the archive/dataframe was not saved to disk on this round of experiments.

    Now, since the enemy agent moves around randomly, that could make sense that there is variation in the archived BCs and what I'm seeing afterwards. A stochastic environment can defeat many algorithms, but to mitigate noisy signals from the environment, I am averaging my BCs and objective values over 5 rollouts per potential agent. Maybe I just need to do more rollouts per candidate solution.

    image

    When I run the extracted agent, I am getting entropy values ~1.224 which, for reference, is very far away in my map and due to that higher entropy, the agents are behaving in what looks to be randomly:

    image

    Steps to Reproduce

    I can send over the full experiment notebook if requested.

    bug 
    opened by aadharna 9
  • [BUG] CVTArchive mishandles its

    [BUG] CVTArchive mishandles its "bins" parameter

    • pyribs version: 0.4.0
    • Python version: 3.6.9, 3.8.3
    • Operating System: Ubuntu 18.04.5 LTS, MacOS 10.15.7

    Description

    Parameter bins of the CVTArchive constructor is incorrectly cast into a tuple, causing np.zeros to reject it as a parameter later on.

    Steps to Reproduce

    import ribs, numpy as np
    archive = ribs.archives.CVTArchive((5, 5, 5, 5, 5), ((0., 1.), (0., 1.), (0., 1.), (0., 1.), (0., 1.)))
    print("There be dragons: ", archive._storage_dims)
    # Prints: There be dragons: ((5, 5, 5, 5, 5),)
    emitters = [ribs.emitters.ImprovementEmitter(archive, np.zeros(5), .5, seed=0, batch_size=20)]
    optimizer = ribs.optimizers.Optimizer(archive, emitters)
    # This crash ensues:
    #  Traceback (most recent call last):
    #    File "<stdin>", line 1, in <module>
    #    File "/usr/local/lib/python3.6/dist-packages/ribs/optimizers/_optimizer.py", line 61, in __init__
    #      self._archive.initialize(self._solution_dim)
    #    File "/usr/local/lib/python3.6/dist-packages/ribs/archives/_cvt_archive.py", line 204, in initialize
    #      ArchiveBase.initialize(self, solution_dim)
    #    File "/usr/local/lib/python3.6/dist-packages/ribs/archives/_archive_base.py", line 350, in initialize
    #      self._occupied = np.zeros(self._storage_dims, dtype=bool)
    #  TypeError: 'tuple' object cannot be interpreted as an integer
    

    Problem

    In class CVTArchive, parameter dims is miscast into a tuple on line 101:

     ArchiveBase.__init__(
                self,
                storage_dims=(bins,), # <<<< HERE
                behavior_dim=len(ranges),
                seed=seed,
                dtype=dtype,
            )
    

    Solution

    Use the same cast as in GridArchive on lines 41 and 48:

            self._dims = np.array(dims) # <<<< HERE
            if len(self._dims) != len(ranges):
                raise ValueError(f"dims (length {len(self._dims)}) and ranges "
                                 f"(length {len(ranges)}) must be the same length")
    
            ArchiveBase.__init__(
                self,
                storage_dims=tuple(self._dims), # <<<< HERE
                behavior_dim=len(self._dims),
                seed=seed,
                dtype=dtype,
            )
    
    bug 
    opened by twoletters 5
  • Visualize mesb

    Visualize mesb

    Description

    Add custom function to visualize a 2D SlidingBoundaryArchive.

    TODO

    • [x] Implement the function.
    • [x] Write tests for it.

    Status

    • [x] I have read the guidelines in CONTRIBUTING.rst.
    • [x] I have formatted my code using yapf
    • [x] I have tested my code by running pytest (tox is run in CI/CD)
    • [x] I have linted my code with pylint (Don't worry if there are too many errors)
    • [x] This PR is ready to go
    opened by lunjohnzhang 5
  • [FEATURE REQUEST] Archive of Classes

    [FEATURE REQUEST] Archive of Classes

    Description

    I'm not sure if this makes sense for the QDL QD algorithm, but I would like to generate diversity for multi-label classification. That is, each sample x has a binary vector of labels (length ~15-50). I would like to generate a diversity of these binary vectors. I can probably just use the CVTArchive with one-hot vectors, but I was curious if there is a better way, or if this will make sense with the QDL QD algorithm.

    Thank you!

    enhancement 
    opened by whitead 4
  • Implement CMA-MAE archive thresholds

    Implement CMA-MAE archive thresholds

    Description

    In this PR, we modify archive_base to implement archive thresholds from CMA-MAE.

    In addition to that, we also revive old implementation of add_single, which was removed in (#221) in favor of calling add with an array of a single element. We found it necessary to revive the old implementation so users of pyribs can easily "hack" the add_single function to use their own implementation. Otherwise, the users will either have to come up with their own implementation of add_single, or understand (and "hack") the much more complicated add function.

    In https://github.com/icaros-usc/pyribs/pull/221#issue-1286519732, it was mentioned that add_batch without numba gives roughly 2x speed increase over add_single with numba. Since we did not revive the use of numba in add_single, add_batch now has a roughly 2.5x - 3x speed increase over add_single. For example, cma_me_imp takes ~100s on my computer with add_batch and ~300s with add_single.

    TODO

    • [x] Revive old implementation of add_single in #221.
    • [x] Implement archive threshold single.
    • [x] Implement archive threshold batch.
    • [x] Verify CMA-MAE for add_single.
    • [x] Verify CMA-MAE for add.
    • [x] Verify CMA-MAEGA.

    Testing

    • [x] Add testing for threshold updating.
    • [x] Fix the tests such that they call add_single and add with single solution

    Can you also fix the tests such that they call add_single and add with single solution? We want to make sure we test both implementations, since we currently assume add_single calls add but that is no longer the case.

    • [x] Test for negative threshold_min.
    • [x] Re-run sphere.py test.
    • [x] add_single and add test with different learning rates.

    Status

    • [x] I have read the guidelines in CONTRIBUTING.md
    • [x] I have formatted my code using yapf
    • [x] I have tested my code by running pytest
    • [x] I have linted my code with pylint
    • [x] I have added a one-line description of my change to the changelog in HISTORY.md
    • [x] This PR is ready to go
    opened by itsdawei 4
  • [BUG] Building from source

    [BUG] Building from source

    • pyribs version: 0.3.1
    • Python version: 3.8.10, Conda 4.10.1, pip 21.0.1 (python 3.8)
    • Operating System: MacOS Catalina

    Description

    Hi, I am try to build this from source in a conda environment and get stuck at dask stuff in the LunarLander example. I tried building with just pip and no conda and got the same problems too :/

    I was not able to pip install .[all] in any environment, if that has anything to do with it. Here is what I've done let me know if something is wrong.

    Steps to Reproduce

    Setup

    conda create --name pyribs python=3.8
    git clone https://github.com/icaros-usc/pyribs.git
    cd pyribs
    which pip3                                                                     
    > /opt/anaconda3/envs/pyribs/bin/pip3
    /opt/anaconda3/envs/pyribs/bin/pip3 install .[all]
    > zsh: no matches found: .[all] # <-- I get this error from non-conda installs too...
    /opt/anaconda3/envs/pyribs/bin/pip3 install .
    
    # other dependencies needed to run test/examples.sh
    conda install -c conda-forge fire
    conda install matplotlib
    conda install -c conda-forge alive-progress
    conda install -c conda-forge gym
    conda install -c conda-forge gym-box2d
    conda install -c conda-forge dask-core
    conda install dask distributed
    

    Test

    ± % tests/examples.sh                                                                                                                                                                                                                                             !6335
    + TMPDIR=./examples_tmp
    + '[' '!' -d ./examples_tmp ']'
    + export OPENBLAS_NUM_THREADS=1
    + OPENBLAS_NUM_THREADS=1
    + export OMP_NUM_THREADS=1
    + OMP_NUM_THREADS=1
    + SPHERE_OUTPUT=./examples_tmp/sphere_output
    + python examples/sphere.py map_elites 20 10 ./examples_tmp/sphere_output
    on 10: Iteration 10 | Archive Coverage: 1.141% QD Score: 258194.080
    |████████████████████████████████████████| 10/10 [100%] in 1.5s (6.49/s)
    Algorithm Time (Excludes Logging and Setup): 0.8987681865692139s
    + python examples/sphere.py line_map_elites 20 10 ./examples_tmp/sphere_output
    on 10: Iteration 10 | Archive Coverage: 0.466% QD Score: 106726.478
    |████████████████████████████████████████| 10/10 [100%] in 1.7s (6.01/s)
    Algorithm Time (Excludes Logging and Setup): 1.0788006782531738s
    + python examples/sphere.py cma_me_imp 20 10 ./examples_tmp/sphere_output
    on 10: Iteration 10 | Archive Coverage: 1.381% QD Score: 318960.752
    |████████████████████████████████████████| 10/10 [100%] in 3.8s (2.61/s)
    Algorithm Time (Excludes Logging and Setup): 3.141014814376831s
    + python examples/sphere.py cma_me_imp_mu 20 10 ./examples_tmp/sphere_output
    on 10: Iteration 10 | Archive Coverage: 1.532% QD Score: 354225.362
    |████████████████████████████████████████| 10/10 [100%] in 3.6s (2.74/s)
    Algorithm Time (Excludes Logging and Setup): 3.0505564212799072s
    + python examples/sphere.py cma_me_rd 20 10 ./examples_tmp/sphere_output
    on 10: Iteration 10 | Archive Coverage: 1.730% QD Score: 324428.521
    |████████████████████████████████████████| 10/10 [100%] in 3.7s (2.71/s)
    Algorithm Time (Excludes Logging and Setup): 3.073657751083374s
    + python examples/sphere.py cma_me_rd_mu 20 10 ./examples_tmp/sphere_output
    on 10: Iteration 10 | Archive Coverage: 1.813% QD Score: 324367.066
    |████████████████████████████████████████| 10/10 [100%] in 3.8s (2.61/s)
    Algorithm Time (Excludes Logging and Setup): 3.112123966217041s
    + python examples/sphere.py cma_me_opt 20 10 ./examples_tmp/sphere_output
    on 10: Iteration 10 | Archive Coverage: 1.601% QD Score: 366030.600
    |████████████████████████████████████████| 10/10 [100%] in 3.7s (2.67/s)
    Algorithm Time (Excludes Logging and Setup): 3.054513931274414s
    + python examples/sphere.py cma_me_mixed 20 10 ./examples_tmp/sphere_output
    on 10: Iteration 10 | Archive Coverage: 1.612% QD Score: 340016.277
    |████████████████████████████████████████| 10/10 [100%] in 3.8s (2.65/s)
    Algorithm Time (Excludes Logging and Setup): 3.078763484954834s
    + LUNAR_LANDER_OUTPUT=./examples_tmp/lunar_lander_output
    + python examples/lunar_lander.py --iterations 5 --outdir ./examples_tmp/lunar_lander_output
    > Starting search.
      - Open Dask's dashboard at http://localhost:8787 to monitor workers.
    |⚠                                       | (!) 0/5 [0%] in 0.7s (0.00/s)
    Traceback (most recent call last):
      File "examples/lunar_lander.py", line 401, in <module>
        fire.Fire(lunar_lander_main)
      File "/opt/anaconda3/envs/pyribs/lib/python3.8/site-packages/fire/core.py", line 141, in Fire
        component_trace = _Fire(component, args, parsed_flag_args, context, name)
      File "/opt/anaconda3/envs/pyribs/lib/python3.8/site-packages/fire/core.py", line 466, in _Fire
        component, remaining_args = _CallAndUpdateTrace(
      File "/opt/anaconda3/envs/pyribs/lib/python3.8/site-packages/fire/core.py", line 681, in _CallAndUpdateTrace
        component = fn(*varargs, **kwargs)
      File "examples/lunar_lander.py", line 391, in lunar_lander_main
        metrics = run_search(client, optimizer, env_seed, iterations, log_freq)
      File "examples/lunar_lander.py", line 218, in run_search
        futures = client.map(lambda model: simulate(model, env_seed), sols)
      File "/opt/anaconda3/envs/pyribs/lib/python3.8/site-packages/distributed/client.py", line 1784, in map
        futures = self._graph_to_futures(
      File "/opt/anaconda3/envs/pyribs/lib/python3.8/site-packages/distributed/client.py", line 2581, in _graph_to_futures
        dsk = dsk.__dask_distributed_pack__(self, keyset, annotations)
    TypeError: __dask_distributed_pack__() takes 3 positional arguments but 4 were given
    (pyribs)
    
    bug 
    opened by agaier 3
  • [BUG] Lunar Lander Example does not work

    [BUG] Lunar Lander Example does not work

    • pyribs version: 0.2.1
    • Python version: 3.8
    • Operating System: Ubuntu 20.04

    Description

    The seaborn dependency is not installed when using pip install -e .[examples]

    This leads to the following error:

    Traceback (most recent call last):
      File "lunar_lander.py", line 15, in <module>
        import seaborn as sns
    ModuleNotFoundError: No module named 'seaborn'
    

    Steps to Reproduce

    1. Clone repository
    2. run pip install -e .[examples] to install depedencies for examples
    3. cd examples
    4. python lunar_lander.py
    bug 
    opened by Bam4d 3
  • MAP-Elites with Sliding Boundaries

    MAP-Elites with Sliding Boundaries

    Description

    Implementation, tests, benchmark of MAP-Elites with Sliding Boundaries based on Hearthstone Deck Space paper.

    TODO

    • [x] Implement
    • [x] Test
    • [x] Benchmark
    • [x] Document
    • [ ] Numba-ize

    Questions

    • [ ] May need help with integrating numba.

    Status

    • [x] I have read the guidelines in CONTRIBUTING.rst.
    • [x] I have formatted my code using yapf
    • [x] I have tested my code by running pytest (tox is run in CI/CD)
    • [x] I have linted my code with pylint (Don't worry if there are too many errors)
    • [x] This PR is ready to go
    opened by lunjohnzhang 3
  • Sliding Boundaries Archive Heatmap Boundaries [BUG]

    Sliding Boundaries Archive Heatmap Boundaries [BUG]

    • pyribs version: 0.4.0
    • Python version: 3.9
    • Operating System: Windows

    Description

    Heyho,

    I am trying to plot a heatmap of a sliding boundaries archive that shows the boundaries. Therefore, I set the parameter boundary_lw to a value large than 0.

    The following picture shows the result of the function call (Fig. 1): image

    But what I would like to have the following (Fig. 2): image

    Steps to Reproduce

    The function call I have used is:

    from ribs.visualize import sliding_boundaries_archive_heatmap
    import matplotlib.pyplot as plt
    fig = plt.figure(figsize=(8, 6))
    sliding_boundaries_archive_heatmap(archive, boundary_lw=0.5, ax=ax1)
    plt.show()
    

    I can not share the archive I am using.

    Potential Fix

    I found the following solution while looking at the code.

    The current implementation of plotting the boundaries is the following:

    if boundary_lw > 0.0:
        ax.vlines(x_boundary,
                  lower_bounds[0],
                  upper_bounds[0],
                  color='k',
                  linewidth=boundary_lw)
        ax.hlines(y_boundary,
                  lower_bounds[1],
                  upper_bounds[1],
                  color='k',
                  linewidth=boundary_lw)
    

    However, to my understanding of the code, we would need to switch the indices. That is, to plot the boundaries correctly, we need the lower and upper bounds of the other dimension. Hence, the code should be:

    if boundary_lw > 0.0:
        ax.vlines(x_boundary,
                  lower_bounds[1],
                  upper_bounds[1],
                  color='k',
                  linewidth=boundary_lw)
        ax.hlines(y_boundary,
                  lower_bounds[0],
                  upper_bounds[0],
                  color='k',
                  linewidth=boundary_lw)
    

    I have used this code to produce Figure 2.

    I am not sure if I am just using the code incorrectly or if this is a bug. I would be happy to hear feedback.

    bug 
    opened by LennartPurucker 2
  • Major API Changes for Emitter

    Major API Changes for Emitter

    Description

    This PR introduces major API changes for the Emitter class. Emitter class now requires a Ranker object and a Selector object.

    The Ranker object will define a rank function which returns the index of the solutions in the descending order that they should be in. It will also define a reset function which resets the internal states of the object.

    RandomDirectionEmitter, OptimizingEmitter, and ImprovementEmitter are now all combined into EvolutionStrategyEmitter, which can be configured to use different Ranker's.

    We also introduce FilterSelector and MuSelector objects which defines a select() function that decides how many parents to select from the potential parents.

    EvolutionStrategyEmitter(x0, sigma0, ranker=Ranker, string, selector, ES, restart_rule, bounds, batch_size, seed) If ranker=str, then we create the object for you

    TODO

    Rankers

    • [x] Implement EvolutionStrategyEmitter
    • [ ] Implement ImprovementRanker
    • [x] Implement TwoStageImprovementRanker
    • [x] Implement RandomDirectionRanker
    • [x] Implement TwoStageRandomDirectionRanker
    • [x] Implement ObjectiveRanker
    • [x] Implement TwoStageObjectiveRanker
    • [x] Implement get_ranker(str) -> Ranker

    Selector

    • [x] MuSelector
    • [x] FilterSelector

    RestartChecker

    • [ ] come up with an API for this

    Others

    • [ ] Add documentation for all new classes/functions
    • [x] Handle repeating docstrings using methods of https://github.com/mwaskom/seaborn/blob/9d8ce6ad4ab213994f0bc84d0c46869df7be0b49/seaborn/relational.py#L719

    Status

    • [ ] I have read the guidelines in CONTRIBUTING.md.
    • [ ] I have formatted my code using yapf
    • [ ] I have tested my code by running pytest
    • [ ] I have linted my code with pylint
    • [ ] I have added a one-line description of my change to the changelog in HISTORY.md.
    • [ ] This PR is ready to go
    API 
    opened by itsdawei 2
  • Edit tutorials to improve flow

    Edit tutorials to improve flow

    Description

    TODO

    Questions

    Status

    • [x] I have read the guidelines in CONTRIBUTING.md
    • [x] I have formatted my code using yapf
    • [x] I have tested my code by running pytest
    • [x] I have linted my code with pylint
    • [ ] I have added a one-line description of my change to the changelog in HISTORY.md
    • [ ] This PR is ready to go
    opened by btjanaka 0
  • [FEATURE REQUEST] Adjustments to use negative objectives (cost)?

    [FEATURE REQUEST] Adjustments to use negative objectives (cost)?

    Description

    It would be nice to have an example where it's a minimization (of a cost) problem instead of the usual maximization (of a reward). The most straight forward approach is to use the negative cost as the objective, but that poses an issue with the usual QD score metric where 0 is given to unexplored cells. Presumably the normalized QD score should address this here:

    norm_qd_score=self.dtype(new_qd_score / self.cells),
    

    but I had to look into the code to confirm whether it would make sense with negative objectives or not, and there may be other cases that implicitly depend on a non-negative objective. Even then, the metric would start with 0, jump to a high negative, and slowly get closer to 0 from the negatives. This is less compelling than a monotonically increasing metric.

    The examples seem to use best - worst performance, which would work if the worst possible performance was known ahead of time and was bounded. But this is not the case in my problem. Switching it to best - worst as of now would not be valuable since it could be inflated by just finding a terrible solution.

    You could also use 1/cost as the objective, but this would stretch the optimizing landscape and would lead to very different behavior.

    It would be nice to have a tutorial or example with best practices for minimization problems, and things to watch out for.

    enhancement 
    opened by LemonPi 1
  • Adding DQD Tutorial

    Adding DQD Tutorial

    Description

    Adding DQD Tutorial https://colab.research.google.com/drive/1DCSFluMmquFPZ2rgUQWA44IPcu7EBfUJ#scrollTo=cr-94yAuzYns

    • [x] I have read the guidelines in CONTRIBUTING.md
    • [x] I have formatted my code using yapf
    • [x] I have tested my code by running pytest
    • [x] I have linted my code with pylint
    • [x] I have added a one-line description of my change to the changelog in HISTORY.md
    • [x] This PR is ready to go
    opened by Nivedit24 2
  • DQDogs Tutorial

    DQDogs Tutorial

    Description

    Write a tutorial for using cma_mae to generate images of dogs.

    TODO

    Status

    • [ ] I have read the guidelines in CONTRIBUTING.md
    • [ ] I have formatted my code using yapf
    • [ ] I have tested my code by running pytest
    • [ ] I have linted my code with pylint
    • [ ] I have added a one-line description of my change to the changelog in HISTORY.md
    • [ ] This PR is ready to go
    opened by itsdawei 0
  • Allow `sigma0` in `EvolutionStrategyEmitter` to be a vector

    Allow `sigma0` in `EvolutionStrategyEmitter` to be a vector

    Description

    Allow sigma0 in EvolutionStrategyEmitter to be a vector.

    TODO

    • [ ] Figure out how to allow sigma to be a vector in CMA-ES.

    Status

    • [x] I have read the guidelines in CONTRIBUTING.md
    • [x] I have formatted my code using yapf
    • [x] I have tested my code by running pytest
    • [x] I have linted my code with pylint
    • [x] I have added a one-line description of my change to the changelog in HISTORY.md
    • [x] This PR is ready to go
    opened by itsdawei 0
  • [FEATURE REQUEST] Separable objs and heatmap metrics

    [FEATURE REQUEST] Separable objs and heatmap metrics

    Hi! I would like to request a feature for separable optimization objective and heatmap metrics. For example, now I want to optimize for the loss which provides more training signal but visualize the accuracy which is more interpretable, but currently only the training objective could be visualized.

    Thanks so much, I really appreciate your hard work!

    enhancement 
    opened by lucys0 0
Releases(v0.4.0)
  • v0.4.0(Jul 22, 2021)

    To learn about this release, see our blog post: https://pyribs.org/blog/0-4-0

    Changelog

    API

    • Add ribs.visualize.parallel_axes_plot for analyzing archives with high-dimensional BCs (#92)
    • Backwards-incompatible: Reduce attributes and parameters in EmitterBase to make it easier to extend (#101)
    • In Optimizer, support emitters that return any number of solutions in ask() (#101)
    • Backwards-incompatible: Store metadata in archives as described in #87 (#103, #114, #115, #119)
    • Backwards-incompatible: Rename "index" to "index_0" in CVTArchive.as_pandas for API consistency (#113)
    • Backwards-incompatible: Make get_index() public in archives to emphasize each index's meaning (#128)
    • Backwards-incompatible: Add index to get_random_elite() and elite_with_behavior() in archives (#129)
    • Add clear() method to archive (#140, #146)
    • Represent archive elites with an Elite namedtuple (#142)
    • Add len and iter methods to archives (#151, #152)
    • Add statistics to archives (#100, #157)
    • Improve manipulation of elites by modifying as_pandas (#123, #149, #153, #158, #168)
    • Add checks for optimizer array and list shapes (#166)

    Documentation

    • Add bibtex citations for tutorials (#122)
    • Remove network training from Fooling MNIST tutorial (#161)
    • Fix video display for lunar lander in Colab (#163)
    • Fix Colab links in stable docs (#164)

    Improvements

    • Add support for Python 3.9 (#84)
    • Test with pinned versions (#110)
    • Increase minimum required versions for scipy and numba (#110)
    • Refactor as_pandas tests (#114)
    • Expand CI/CD to test examples and tutorials (#117)
    • Tidy up existing tests (#120, #127)
    • Fix vocab in various areas (#138)
    • Fix dependency issues in tests (#139)
    • Remove tox from CI (#143)
    • Replace "entry" with "elite" in tests (#144)
    • Use new archive API in ribs.visualize implementation (#155)
    Source code(tar.gz)
    Source code(zip)
  • v0.3.1(Mar 6, 2021)

    This release features various bug fixes and improvements. In particular, we have added tests for SlidingBoundariesArchive and believe it is ready for more rigorous use.

    Changelog

    • Move SlidingBoundariesArchive out of experimental by adding tests and fixing bugs (#93)
    • Added nicer figures to the Sphere example with grid_archive_heatmap (#86)
    • Added testing for Windows and MacOS (#83)
    • Fixed package metadata e.g. description
    Source code(tar.gz)
    Source code(zip)
  • v0.3.0(Feb 6, 2021)

    pyribs is now in beta. Since our alpha release (0.2.0), we have polished the library and added new tutorials and examples to our documentation.

    Changelog

    • Added a Lunar Lander example that extends the lunar lander tutorial (#70)
    • Added New Tutorial: Illuminating the Latent Space of an MNIST GAN (#78)
    • GridArchive: Added a boundaries attribute with the upper and lower bounds of each dimension's bins (#76)
    • Fixed a bug where CMA-ME emitters do not work with float32 archives (#74)
    • Fixed a bug where Optimizer is able to take in non-unique emitter instances (#75)
    • Fixed a bug where GridArchive failed for float32 due to a small epsilon (#81)
    • Fix issues with bounds in the SlidingBoundaryArchive (#77)
    • Added clearer error messages for archives (#82)
    • Modified the Python requirements to allow any version above 3.6.0 (#68)
    • The wheel is now fixed so that it only supports py3 rather than py2 and py3 (#68)
    • Miscellaneous documentation fixes (#71)
    Source code(tar.gz)
    Source code(zip)
  • v0.2.1(Jan 30, 2021)

  • v0.2.0(Jan 29, 2021)

Owner
ICAROS
Interactive and Collaborative Autonomous Robotic Systems
ICAROS
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