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
Automatic tool focused on deriving metallicities of open clusters

metalcode Automatic tool focused on deriving metallicities of open clusters. Based on the method described in Pöhnl & Paunzen (2010, https://ui.adsabs

2 Dec 13, 2021
Official implementation of the article "Unsupervised JPEG Domain Adaptation For Practical Digital Forensics"

Unsupervised JPEG Domain Adaptation for Practical Digital Image Forensics @WIFS2021 (Montpellier, France) Rony Abecidan, Vincent Itier, Jeremie Boulan

Rony Abecidan 6 Jan 06, 2023
A tutorial on DataFrames.jl prepared for JuliaCon2021

JuliaCon2021 DataFrames.jl Tutorial This is a tutorial on DataFrames.jl prepared for JuliaCon2021. A video recording of the tutorial is available here

Bogumił Kamiński 106 Jan 09, 2023
Ros2-voiceroid2 - ROS2 wrapper package of VOICEROID2

ros2_voiceroid2 ROS2 wrapper package of VOICEROID2 Windows Only Installation Ins

Nkyoku 1 Jan 23, 2022
StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion

StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion Yinghao Aaron Li, Ali Zare, Nima Mesgarani We pres

Aaron (Yinghao) Li 282 Jan 01, 2023
Objax Apache-2Objax (🥉19 · ⭐ 580) - Objax is a machine learning framework that provides an Object.. Apache-2 jax

Objax Tutorials | Install | Documentation | Philosophy This is not an officially supported Google product. Objax is an open source machine learning fr

Google 729 Jan 02, 2023
Internship Assessment Task for BaggageAI.

BaggageAI Internship Task Problem Statement: You are given two sets of images:- background and threat objects. Background images are the background x-

Arya Shah 10 Nov 14, 2022
Reinforcement learning algorithms in RLlib

raylab Reinforcement learning algorithms in RLlib and PyTorch. Installation pip install raylab Quickstart Raylab provides agents and environments to b

Ângelo 50 Sep 08, 2022
Generative Models as a Data Source for Multiview Representation Learning

GenRep Project Page | Paper Generative Models as a Data Source for Multiview Representation Learning Ali Jahanian, Xavier Puig, Yonglong Tian, Phillip

Ali 81 Dec 03, 2022
Pytorch implementation of the paper Progressive Growing of Points with Tree-structured Generators (BMVC 2021)

PGpoints Pytorch implementation of the paper Progressive Growing of Points with Tree-structured Generators (BMVC 2021) Hyeontae Son, Young Min Kim Pre

Hyeontae Son 9 Jun 06, 2022
PyTorch Implementation for AAAI'21 "Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection"

UMS for Multi-turn Response Selection Implements the model described in the following paper Do Response Selection Models Really Know What's Next? Utte

Taesun Whang 47 Nov 22, 2022
EdiBERT, a generative model for image editing

EdiBERT, a generative model for image editing EdiBERT is a generative model based on a bi-directional transformer, suited for image manipulation. The

16 Dec 07, 2022
This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"

Fisher Information Loss This repository contains code that can be used to reproduce the experimental results presented in the paper: Awni Hannun, Chua

Facebook Research 43 Dec 30, 2022
Dcf-game-infrastructure-public - Contains all the components necessary to run a DC finals (attack-defense CTF) game from OOO

dcf-game-infrastructure All the components necessary to run a game of the OOO DC

Order of the Overflow 46 Sep 13, 2022
Simple embedding based text classifier inspired by fastText, implemented in tensorflow

FastText in Tensorflow This project is based on the ideas in Facebook's FastText but implemented in Tensorflow. However, it is not an exact replica of

Alan Patterson 306 Dec 02, 2022
Space Time Recurrent Memory Network - Pytorch

Space Time Recurrent Memory Network - Pytorch (wip) Implementation of Space Time Recurrent Memory Network, recurrent network competitive with attentio

Phil Wang 50 Nov 07, 2021
This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

Graphormer By Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng*, Guolin Ke, Di He*, Yanming Shen and Tie-Yan Liu. This repo is the official impl

Microsoft 1.3k Dec 29, 2022
Official Repository for the paper "Improving Baselines in the Wild".

iWildCam and FMoW baselines (WILDS) This repository was originally forked from the official repository of WILDS datasets (commit 7e103ed) For general

Kazuki Irie 3 Nov 24, 2022
MAterial del programa Misión TIC 2022

Mision TIC 2022 Esta iniciativa, aparece como respuesta frente a los retos de la Cuarta Revolución Industrial, y tiene como objetivo la formación de 1

6 May 25, 2022
Implementations of LSTM: A Search Space Odyssey variants and their training results on the PTB dataset.

An LSTM Odyssey Code for training variants of "LSTM: A Search Space Odyssey" on Fomoro. Check out the blog post. Training Install TensorFlow. Clone th

Fomoro AI 95 Apr 13, 2022