Performant, differentiable reinforcement learning

Related tags

Deep Learningdeluca
Overview

deluca

Performant, differentiable reinforcement learning

Notes

  1. This is pre-alpha software and is undergoing a number of core changes. Updates to follow.
  2. Please see the examples for guidance on how to use deluca

pypi pyversions security: bandit Code style: black License: Apache 2.0

build coverage Documentation Status doc_coverage

deluca

Comments
  • Exception error during installing deluca

    Exception error during installing deluca

    Hi.

    I am trying to install deluca and I get an Exception error. I am using

    Ubuntu 64 on a virtual machine Pycharm CE 2021.2, Python 3.8 pip 212.1.2

    I tried to install deluca with the package manager in Pycharm, the terminal in Pycharm and also the Ubuntu terminal. The error is the same. Note that I can install other normal packages like Numpy, Scipy, etc with no problem. Thanks in advance and I am looking forward to using this amazing package!

    pip install deluca
    Collecting deluca
       Using cached deluca-0.0.17-py3-none-any.whl (52 kB)
    Collecting flax
       Using cached flax-0.3.4-py3-none-any.whl (183 kB)
    Collecting brax
       Using cached brax-0.0.4-py3-none-any.whl (117 kB)
    Processing
    ./.cache/pip/wheels/78/ae/07/bd3adac873fa80efc909c09331831905ac657dbb8d1278235e/jax-0.2.19-py3-none-any.whl
    Collecting optax
       Using cached optax-0.0.9-py3-none-any.whl (118 kB)
    Collecting scipy
       Using cached
    scipy-1.7.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (28.4 MB)
    Collecting numpy
       Using cached
    numpy-1.21.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
    (15.8 MB)
    Collecting matplotlib
       Using cached matplotlib-3.4.3-cp38-cp38-manylinux1_x86_64.whl (10.3 MB)
    Collecting msgpack
       Using cached msgpack-1.0.2-cp38-cp38-manylinux1_x86_64.whl (302 kB)
    Collecting grpcio
       Using cached grpcio-1.39.0-cp38-cp38-manylinux2014_x86_64.whl (4.3 MB)
    Collecting clu
       Using cached clu-0.0.6-py3-none-any.whl (77 kB)
    Collecting gym
       Using cached gym-0.19.0.tar.gz (1.6 MB)
    Collecting absl-py
       Using cached absl_py-0.13.0-py3-none-any.whl (132 kB)
    Collecting tfp-nightly[jax]<=0.13.0.dev20210422
       Using cached tfp_nightly-0.13.0.dev20210422-py2.py3-none-any.whl (5.3 MB)
    Collecting jaxlib
       Using cached jaxlib-0.1.70-cp38-none-manylinux2010_x86_64.whl (46.9 MB)
    Collecting dataclasses
       Using cached dataclasses-0.6-py3-none-any.whl (14 kB)
    Collecting opt-einsum
       Using cached opt_einsum-3.3.0-py3-none-any.whl (65 kB)
    Collecting chex>=0.0.4
       Using cached chex-0.0.8-py3-none-any.whl (57 kB)
    Requirement already satisfied: pillow>=6.2.0 in
    /usr/lib/python3/dist-packages (from matplotlib->flax->deluca) (7.0.0)
    Collecting cycler>=0.10
       Using cached cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)
    Collecting pyparsing>=2.2.1
       Using cached pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)
    Collecting kiwisolver>=1.0.1
       Using cached kiwisolver-1.3.1-cp38-cp38-manylinux1_x86_64.whl (1.2 MB)
    Requirement already satisfied: python-dateutil>=2.7 in
    /usr/lib/python3/dist-packages (from matplotlib->flax->deluca) (2.7.3)
    Requirement already satisfied: six>=1.5.2 in
    /usr/lib/python3/dist-packages (from grpcio->brax->deluca) (1.14.0)
    Collecting tensorflow-datasets
       Using cached tensorflow_datasets-4.4.0-py3-none-any.whl (4.0 MB)
    Collecting packaging
       Using cached packaging-21.0-py3-none-any.whl (40 kB)
    Collecting ml-collections
       Using cached ml_collections-0.1.0-py3-none-any.whl (88 kB)
    Collecting tensorflow
       Downloading tensorflow-2.6.0-cp38-cp38-manylinux2010_x86_64.whl
    (458.4 MB)
          |▋                               | 8.4 MB 16 kB/s eta
    7:44:54ERROR: Exception:
    Traceback (most recent call last):
       File
    "/usr/share/python-wheels/urllib3-1.25.8-py2.py3-none-any.whl/urllib3/response.py",
    line 425, in _error_catcher
         yield
       File
    "/usr/share/python-wheels/urllib3-1.25.8-py2.py3-none-any.whl/urllib3/response.py",
    line 507, in read
         data = self._fp.read(amt) if not fp_closed else b""
       File
    "/usr/share/python-wheels/CacheControl-0.12.6-py2.py3-none-any.whl/cachecontrol/filewrapper.py",
    line 62, in read
         data = self.__fp.read(amt)
       File "/usr/lib/python3.8/http/client.py", line 455, in read
         n = self.readinto(b)
       File "/usr/lib/python3.8/http/client.py", line 499, in readinto
         n = self.fp.readinto(b)
       File "/usr/lib/python3.8/socket.py", line 669, in readinto
         return self._sock.recv_into(b)
       File "/usr/lib/python3.8/ssl.py", line 1241, in recv_into
         return self.read(nbytes, buffer)
       File "/usr/lib/python3.8/ssl.py", line 1099, in read
         return self._sslobj.read(len, buffer)
    socket.timeout: The read operation timed out
    
    During handling of the above exception, another exception occurred:
    
    Traceback (most recent call last):
       File
    "/usr/lib/python3/dist-packages/pip/_internal/cli/base_command.py", line
    186, in _main
         status = self.run(options, args)
       File
    "/usr/lib/python3/dist-packages/pip/_internal/commands/install.py", line
    357, in run
         resolver.resolve(requirement_set)
       File
    "/usr/lib/python3/dist-packages/pip/_internal/legacy_resolve.py", line
    177, in resolve
         discovered_reqs.extend(self._resolve_one(requirement_set, req))
       File
    "/usr/lib/python3/dist-packages/pip/_internal/legacy_resolve.py", line
    333, in _resolve_one
         abstract_dist = self._get_abstract_dist_for(req_to_install)
       File
    "/usr/lib/python3/dist-packages/pip/_internal/legacy_resolve.py", line
    282, in _get_abstract_dist_for
         abstract_dist = self.preparer.prepare_linked_requirement(req)
       File
    "/usr/lib/python3/dist-packages/pip/_internal/operations/prepare.py",
    line 480, in prepare_linked_requirement
         local_path = unpack_url(
       File
    "/usr/lib/python3/dist-packages/pip/_internal/operations/prepare.py",
    line 282, in unpack_url
         return unpack_http_url(
       File
    "/usr/lib/python3/dist-packages/pip/_internal/operations/prepare.py",
    line 158, in unpack_http_url
         from_path, content_type = _download_http_url(
       File
    "/usr/lib/python3/dist-packages/pip/_internal/operations/prepare.py",
    line 303, in _download_http_url
         for chunk in download.chunks:
       File "/usr/lib/python3/dist-packages/pip/_internal/utils/ui.py", line
    160, in iter
         for x in it:
       File "/usr/lib/python3/dist-packages/pip/_internal/network/utils.py",
    line 15, in response_chunks
         for chunk in response.raw.stream(
       File
    "/usr/share/python-wheels/urllib3-1.25.8-py2.py3-none-any.whl/urllib3/response.py",
    line 564, in stream
         data = self.read(amt=amt, decode_content=decode_content)
       File
    "/usr/share/python-wheels/urllib3-1.25.8-py2.py3-none-any.whl/urllib3/response.py",
    line 529, in read
         raise IncompleteRead(self._fp_bytes_read, self.length_remaining)
       File "/usr/lib/python3.8/contextlib.py", line 131, in __exit__
         self.gen.throw(type, value, traceback)
       File
    "/usr/share/python-wheels/urllib3-1.25.8-py2.py3-none-any.whl/urllib3/response.py",
    line 430, in _error_catcher
         raise ReadTimeoutError(self._pool, None, "Read timed out.")
    urllib3.exceptions.ReadTimeoutError:
    HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Read timed
    out.
    
    opened by FarnazAdib 4
  • Internal change

    Internal change

    Internal change

    FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/google/deluca/pull/57 from google:inverse_map baa4932444495538d91151653165cdcb386b52fc

    opened by copybara-service[bot] 0
  • Internal change

    Internal change

    Internal change

    FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/google/deluca/pull/57 from google:inverse_map baa4932444495538d91151653165cdcb386b52fc

    opened by copybara-service[bot] 0
  • Internal change

    Internal change

    Internal change

    FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/google/deluca/pull/57 from google:inverse_map baa4932444495538d91151653165cdcb386b52fc

    cla: yes 
    opened by copybara-service[bot] 0
  • Internal change

    Internal change

    Internal change

    FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/google/deluca/pull/57 from google:inverse_map baa4932444495538d91151653165cdcb386b52fc

    cla: yes 
    opened by copybara-service[bot] 0
  • Consider dependency on OpenAI Gym

    Consider dependency on OpenAI Gym

    • Not clear what the benefits of compatibility are since existing agents that work on OpenAI Gym environments have no guarantee of working on deluca environments
    • OpenAI Gym bundles environment with initialization and task. Not necessarily something we want to do.
    opened by danielsuo 0
  • Changes to _adaptive.py

    Changes to _adaptive.py

    Hello! I made some modifications to AdaGPC (in _adaptive.py). In the existing implementation, GPC outperforms AdaGPC in the known LDS setting, which is the opposite of what one should expect. Based on some preliminary experiments, I believe AdaGPC is now working properly (at least in the known dynamics version). (I also made some miscellaneous changes in other files, e.g., to the imports in some of the agent files -- I think there might have been some file restructuring across different versions of deluca, but the imports were not updated to reflect this change, causing some errors at runtime.) Please let me know if you have any questions/concerns. Thanks!

    opened by simran135 1
  • [JAX] Avoid private implementation detail _ScalarMeta.

    [JAX] Avoid private implementation detail _ScalarMeta.

    [JAX] Avoid private implementation detail _ScalarMeta.

    The closest public approximation to type(jnp.float32) is type[Any]. Nothing is ever actually an instance of one of these types, either (they build DeviceArrays if instantiated.)

    opened by copybara-service[bot] 0
  • [JAX] Avoid private implementation detail _ScalarMeta.

    [JAX] Avoid private implementation detail _ScalarMeta.

    [JAX] Avoid private implementation detail _ScalarMeta.

    The closest public approximation to type(jnp.float32) is type[Any]. Nothing is ever actually an instance of one of these types, either (they build DeviceArrays if instantiated.)

    opened by copybara-service[bot] 0
  • Internal change

    Internal change

    Internal change

    FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/google/deluca/pull/57 from google:inverse_map baa4932444495538d91151653165cdcb386b52fc

    opened by copybara-service[bot] 0
  • Implementation of drc

    Implementation of drc

    Hi

    Thanks for providing this interesting package.

    I am trying to test drc on a simple setup and I notice that the current implementation of drc does not work. I mean when I try it for a simple partially observable linear system with A = np.array([[1.0 0.95], [0.0, -0.9]]), B = np.array([[0.0], [1.0]]) C = np.array([[1.0, 0]]) Q , R = I gaussian process noise, zero observation noise which is open loop stable, the controller acts like a zero controller. I tried to get a different response by setting the hyperparameters but they are mostly the same. Then I looked at the implementation at the deluca github and I noticed that the counterfactual cost is not defined correctly (if I am not wrong). According to Algorithm 1 in [1], we need to use M_t to compute y_t (which depends on the previous controls (u) using again M_t) but in the implementation, the previous controls based on M_{t-i} are used. Anyway, I implemented the algorithm using M_t but what I get after the simulation is either close to zero control or an unstable one.

    I was wondering if you have any code example for the DRC algorithm that works? [1] Simchowitz, Max and Singh, Karan and Hazan, Elad, "Improper learning for non-stochastic control", COLT 2020.

    Thanks a lot, Sincerely, Farnaz

    opened by FarnazAdib 4
Releases(v0.0.17)
Owner
Google
Google ❤️ Open Source
Google
Submodular Subset Selection for Active Domain Adaptation (ICCV 2021)

S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation ICCV 2021 Harsh Rangwani, Arihant Jain*, Sumukh K Aithal*, R. Ve

Video Analytics Lab -- IISc 13 Dec 28, 2022
A Pytorch loader for MVTecAD dataset.

MVTecAD A Pytorch loader for MVTecAD dataset. It strictly follows the code style of common Pytorch datasets, such as torchvision.datasets.CIFAR10. The

Jiyuan 1 Dec 27, 2021
Algorithmic trading with deep learning experiments

Deep-Trading Algorithmic trading with deep learning experiments. Now released part one - simple time series forecasting. I plan to implement more soph

Alex Honchar 1.4k Jan 02, 2023
Standalone pre-training recipe with JAX+Flax

Sabertooth Sabertooth is standalone pre-training recipe based on JAX+Flax, with data pipelines implemented in Rust. It runs on CPU, GPU, and/or TPU, b

Nikita Kitaev 26 Nov 28, 2022
A minimal TPU compatible Jax implementation of NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

NeRF Minimal Jax implementation of NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. Result of Tiny-NeRF RGB Depth

Soumik Rakshit 11 Jul 24, 2022
A Python library for Deep Probabilistic Modeling

Abstract DeeProb-kit is a Python library that implements deep probabilistic models such as various kinds of Sum-Product Networks, Normalizing Flows an

DeeProb-org 46 Dec 26, 2022
The AWS Certified SysOps Administrator

The AWS Certified SysOps Administrator – Associate (SOA-C02) exam is intended for system administrators in a cloud operations role who have at least 1 year of hands-on experience with deployment, man

Aiden Pearce 32 Dec 11, 2022
PyTorch implementation of the paper Dynamic Data Augmentation with Gating Networks

Dynamic Data Augmentation with Gating Networks This is an official PyTorch implementation of the paper Dynamic Data Augmentation with Gating Networks

九州大学 ヒューマンインタフェース研究室 3 Oct 26, 2022
Official repository of "BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment"

BasicVSR_PlusPlus (CVPR 2022) [Paper] [Project Page] [Code] This is the official repository for BasicVSR++. Please feel free to raise issue related to

Kelvin C.K. Chan 227 Jan 01, 2023
Keras Realtime Multi-Person Pose Estimation - Keras version of Realtime Multi-Person Pose Estimation project

This repository has become incompatible with the latest and recommended version of Tensorflow 2.0 Instead of refactoring this code painfully, I create

M Faber 769 Dec 08, 2022
Do Neural Networks for Segmentation Understand Insideness?

This is part of the code to reproduce the results of the paper Do Neural Networks for Segmentation Understand Insideness? [pdf] by K. Villalobos (*),

biolins 0 Mar 20, 2021
Deep Unsupervised 3D SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment.

(ACMMM 2021 Oral) SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment This repository shows two tasks: Face landmark detection and Fac

BoomStar 51 Dec 13, 2022
Implementation of StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation in PyTorch

StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation Implementation of StyleSpace Analysis: Disentangled Controls for StyleGAN Ima

Xuanchi Ren 86 Dec 07, 2022
Visyerres sgdf woob - Modules Woob pour l'intranet et autres sites Scouts et Guides de France

Vis'Yerres SGDF - Modules Woob Vous avez le sentiment que l'intranet des Scouts

Thomas Touhey (pas un pseudonyme) 3 Dec 24, 2022
Activity image-based video retrieval

Cross-modal-retrieval Our approach is focus on Activity Image-to-Video Retrieval (AIVR) task. The compared methods are state-of-the-art single modalit

BCMI 75 Oct 21, 2021
PyTorch implementation of MoCo: Momentum Contrast for Unsupervised Visual Representation Learning

MoCo: Momentum Contrast for Unsupervised Visual Representation Learning This is a PyTorch implementation of the MoCo paper: @Article{he2019moco, aut

Meta Research 3.7k Jan 02, 2023
RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation (CIKM'17)

RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation This is the implementation of RATE: Overcoming Noise and Spar

Yu Zhang 5 Feb 10, 2022
Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋

How to eat TensorFlow2 in 30 days ? 🔥 🔥 Click here for Chinese Version(中文版) 《10天吃掉那只pyspark》 🚀 github项目地址: https://github.com/lyhue1991/eat_pyspark

lyhue1991 9.7k Jan 01, 2023
Code for "Unsupervised State Representation Learning in Atari"

Unsupervised State Representation Learning in Atari Ankesh Anand*, Evan Racah*, Sherjil Ozair*, Yoshua Bengio, Marc-Alexandre Côté, R Devon Hjelm This

Mila 217 Jan 03, 2023
Rasterize with the least efforts for researchers.

utils3d Rasterize and do image-based 3D transforms with the least efforts for researchers. Based on numpy and OpenGL. It could be helpful when you wan

Ruicheng Wang 8 Dec 15, 2022