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
Code for the paper One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation, CVPR 2021.

One Thing One Click One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation (CVPR2021) Code for the paper One Thi

44 Dec 12, 2022
PyTorch implementation of normalizing flow models

PyTorch implementation of normalizing flow models

Vincent Stimper 242 Jan 02, 2023
Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

Part-Aware Data Augmentation for 3D Object Detection in Point Cloud This repository contains a reference implementation of our Part-Aware Data Augment

Jaeseok Choi 62 Jan 03, 2023
The official implementation of NeurIPS 2021 paper: Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

Introduction This repository includes the source code for "Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks", which is pu

machen 11 Nov 27, 2022
CM building dataset Timisoara

CM_building_dataset_Timisoara Date created: Febr-2020 The Timi\c{s}oara Building Dataset - TMBuD - is composed of 160 images with the resolution of 76

Orhei Ciprian 5 Sep 07, 2022
Deep Residual Networks with 1K Layers

Deep Residual Networks with 1K Layers By Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Microsoft Research Asia (MSRA). Table of Contents Introduc

Kaiming He 856 Jan 06, 2023
Semi-Autoregressive Transformer for Image Captioning

Semi-Autoregressive Transformer for Image Captioning Requirements Python 3.6 Pytorch 1.6 Prepare data Please use git clone --recurse-submodules to clo

YE Zhou 23 Dec 09, 2022
Incomplete easy-to-use math solver and PDF generator.

Math Expert Let me do your work Preview preview.mp4 Introduction Math Expert is our (@salastro, @younis-tarek, @marawn-mogeb) math high school graduat

SalahDin Ahmed 22 Jul 11, 2022
Code for "Multi-Compound Transformer for Accurate Biomedical Image Segmentation"

News The code of MCTrans has been released. if you are interested in contributing to the standardization of the medical image analysis community, plea

97 Jan 05, 2023
This is the official implementation of the paper "Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation".

[CVPRW 2021] - Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation

Anirudh S Chakravarthy 6 May 03, 2022
PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)

Unsupervised Depth Completion with Calibrated Backprojection Layers PyTorch implementation of Unsupervised Depth Completion with Calibrated Backprojec

80 Dec 13, 2022
CVPR 2022 "Online Convolutional Re-parameterization"

OREPA: Online Convolutional Re-parameterization This repo is the PyTorch implementation of our paper to appear in CVPR2022 on "Online Convolutional Re

Mu Hu 121 Dec 21, 2022
Does Pretraining for Summarization Reuqire Knowledge Transfer?

Pretraining summarization models using a corpus of nonsense

Approximately Correct Machine Intelligence (ACMI) Lab 12 Dec 19, 2022
Official repository for the ISBI 2021 paper Transformer Assisted Convolutional Neural Network for Cell Instance Segmentation

SegPC-2021 This is the official repository for the ISBI 2021 paper Transformer Assisted Convolutional Neural Network for Cell Instance Segmentation by

Datascience IIT-ISM 13 Dec 14, 2022
git《Commonsense Knowledge Base Completion with Structural and Semantic Context》(AAAI 2020) GitHub: [fig1]

Commonsense Knowledge Base Completion with Structural and Semantic Context Code for the paper Commonsense Knowledge Base Completion with Structural an

AI2 96 Nov 05, 2022
A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation

Paper Khoi Nguyen, Sinisa Todorovic "A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation", accepted to ICCV 2021 Our code is mai

Khoi Nguyen 5 Aug 14, 2022
Course on computational design, non-linear optimization, and dynamics of soft systems at UIUC.

Computational Design and Dynamics of Soft Systems · This is a repository that contains the source code for generating the lecture notes, handouts, exe

Tejaswin Parthasarathy 4 Jul 21, 2022
Python 3 module to print out long strings of text with intervals of time inbetween

Python-Fastprint Python 3 module to print out long strings of text with intervals of time inbetween Install: pip install fastprint Sync Usage: from fa

Kainoa Kanter 2 Jun 27, 2022
PyTorch code for the paper "Complementarity is the King: Multi-modal and Multi-grained Hierarchical Semantic Enhancement Network for Cross-modal Retrieval".

Complementarity is the King: Multi-modal and Multi-grained Hierarchical Semantic Enhancement Network for Cross-modal Retrieval (M2HSE) PyTorch code fo

Xinlei-Pei 6 Dec 23, 2022
Code, environments, and scripts for the paper: "How Private Is Your RL Policy? An Inverse RL Based Analysis Framework"

Privacy-Aware Inverse RL (PRIL) Analysis Framework Code, environments, and scripts for the paper: "How Private Is Your RL Policy? An Inverse RL Based

1 Dec 06, 2021