8c2aea2add
* exported rl-algs * more stuff from rl-algs * run slow tests * re-exported rl_algs * re-exported rl_algs - fixed problems with serialization test and test_cartpole * replaced atari_arg_parser with common_arg_parser * run.py can run algos from both baselines and rl_algs * added approximate humanoid reward with ppo2 into the README for reference * dummy commit to RUN BENCHMARKS * dummy commit to RUN BENCHMARKS * dummy commit to RUN BENCHMARKS * dummy commit to RUN BENCHMARKS * very dummy commit to RUN BENCHMARKS * serialize variables as a dict, not as a list * running_mean_std uses tensorflow variables * fixed import in vec_normalize * dummy commit to RUN BENCHMARKS * dummy commit to RUN BENCHMARKS * flake8 complaints * save all variables to make sure we save the vec_normalize normalization * benchmarks on ppo2 only RUN BENCHMARKS * make_atari_env compatible with mpi * run ppo_mpi benchmarks only RUN BENCHMARKS * hardcode names of retro environments * add defaults * changed default ppo2 lr schedule to linear RUN BENCHMARKS * non-tf normalization benchmark RUN BENCHMARKS * use ncpu=1 for mujoco sessions - gives a bit of a performance speedup * reverted running_mean_std to user property decorators for mean, var, count * reverted VecNormalize to use RunningMeanStd (no tf) * reverted VecNormalize to use RunningMeanStd (no tf) * profiling wip * use VecNormalize with regular RunningMeanStd * added acer runner (missing import) * flake8 complaints * added a note in README about TfRunningMeanStd and serialization of VecNormalize * dummy commit to RUN BENCHMARKS * merged benchmarks branch
36 lines
1.0 KiB
Python
36 lines
1.0 KiB
Python
from setuptools import setup, find_packages
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import sys
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if sys.version_info.major != 3:
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print('This Python is only compatible with Python 3, but you are running '
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'Python {}. The installation will likely fail.'.format(sys.version_info.major))
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setup(name='baselines',
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packages=[package for package in find_packages()
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if package.startswith('baselines')],
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install_requires=[
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'gym[mujoco,atari,classic_control,robotics]',
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'scipy',
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'tqdm',
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'joblib',
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'dill',
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'progressbar2',
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'mpi4py',
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'cloudpickle',
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'tensorflow>=1.4.0',
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'click',
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'opencv-python'
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],
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extras_require={
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'test': [
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'filelock',
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'pytest'
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]
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},
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description='OpenAI baselines: high quality implementations of reinforcement learning algorithms',
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author='OpenAI',
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url='https://github.com/openai/baselines',
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author_email='gym@openai.com',
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version='0.1.5')
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