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
The Face Mask recognition system uses AI technology to detect the person with or without a mask.

Face Mask Detection Face Mask Detection system built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect

Rohan Kasabe 4 Apr 05, 2022
Language Used: Python . Made in Jupyter(Anaconda) notebook.

FACE-DETECTION-ATTENDENCE-SYSTEM Made in Jupyter(Anaconda) notebook. Language Used: Python Steps to perform before running the program : Install Anaco

1 Jan 12, 2022
Second-order Attention Network for Single Image Super-resolution (CVPR-2019)

Second-order Attention Network for Single Image Super-resolution (CVPR-2019) "Second-order Attention Network for Single Image Super-resolution" is pub

516 Dec 28, 2022
Code for our SIGCOMM'21 paper "Network Planning with Deep Reinforcement Learning".

0. Introduction This repository contains the source code for our SIGCOMM'21 paper "Network Planning with Deep Reinforcement Learning". Notes The netwo

NetX Group 68 Nov 24, 2022
The pytorch implementation of DG-Font: Deformable Generative Networks for Unsupervised Font Generation

DG-Font: Deformable Generative Networks for Unsupervised Font Generation The source code for 'DG-Font: Deformable Generative Networks for Unsupervised

130 Dec 05, 2022
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark

This dataset is a large-scale dataset for moving object detection and tracking in satellite videos, which consists of 40 satellite videos captured by Jilin-1 satellite platforms.

Qingyong 87 Dec 22, 2022
Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

Seulki Park 70 Jan 03, 2023
OpenAi's gym environment wrapper to vectorize them with Ray

Ray Vector Environment Wrapper You would like to use Ray to vectorize your environment but you don't want to use RLLib ? You came to the right place !

Pierre TASSEL 15 Nov 10, 2022
Official Repository for our ICCV2021 paper: Continual Learning on Noisy Data Streams via Self-Purified Replay

Continual Learning on Noisy Data Streams via Self-Purified Replay This repository contains the official PyTorch implementation for our ICCV2021 paper.

Jinseo Jeong 22 Nov 23, 2022
PyTorch implementation of DreamerV2 model-based RL algorithm

PyDreamer Reimplementation of DreamerV2 model-based RL algorithm in PyTorch. The official DreamerV2 implementation can be found here. Features ... Run

118 Dec 15, 2022
FAST-RIR: FAST NEURAL DIFFUSE ROOM IMPULSE RESPONSE GENERATOR

This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.

Anton Jeran Ratnarajah 89 Dec 22, 2022
Model Zoo of BDD100K Dataset

Model Zoo of BDD100K Dataset

ETH VIS Group 200 Dec 27, 2022
A high-performance distributed deep learning system targeting large-scale and automated distributed training.

HETU Documentation | Examples Hetu is a high-performance distributed deep learning system targeting trillions of parameters DL model training, develop

DAIR Lab 150 Dec 21, 2022
PyTorch Implementation of "Light Field Image Super-Resolution with Transformers"

LFT PyTorch implementation of "Light Field Image Super-Resolution with Transformers", arXiv 2021. [pdf]. Contributions: We make the first attempt to a

Squidward 62 Nov 28, 2022
Prefix-Tuning: Optimizing Continuous Prompts for Generation

Prefix Tuning Files: . ├── gpt2 # Code for GPT2 style autoregressive LM │ ├── train_e2e.py # high-level script

530 Jan 04, 2023
Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)

taganomaly Anomaly detection labeling tool, specifically for multiple time series (one time series per category). Taganomaly is a tool for creating la

Microsoft 272 Dec 17, 2022
Code release for paper: The Boombox: Visual Reconstruction from Acoustic Vibrations

The Boombox: Visual Reconstruction from Acoustic Vibrations Boyuan Chen, Mia Chiquier, Hod Lipson, Carl Vondrick Columbia University Project Website |

Boyuan Chen 12 Nov 30, 2022
Point cloud processing tool library.

Point Cloud ToolBox This point cloud processing tool library can be used to process point clouds, 3d meshes, and voxels. Environment python 3.7.5 Dep

ZhangXinyun 40 Dec 09, 2022
A robust camera and Lidar fusion based velocity estimator to undistort the pointcloud.

Lidar with Velocity A robust camera and Lidar fusion based velocity estimator to undistort the pointcloud. related paper: Lidar with Velocity : Motion

ISEE Research Group 164 Dec 30, 2022
PyTorch implementation of MSBG hearing loss model and MBSTOI intelligibility metric

PyTorch implementation of MSBG hearing loss model and MBSTOI intelligibility metric This repository contains the implementation of MSBG hearing loss m

BUT <a href=[email protected]"> 9 Nov 08, 2022