deluca
Performant, differentiable reinforcement learning
Notes
- This is pre-alpha software and is undergoing a number of core changes. Updates to follow.
- Please see the examples for guidance on how to use
deluca
deluca
Performant, differentiable reinforcement learning
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.
Internal change
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/google/deluca/pull/57 from google:inverse_map baa4932444495538d91151653165cdcb386b52fc
Internal change
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/google/deluca/pull/57 from google:inverse_map baa4932444495538d91151653165cdcb386b52fc
Internal change
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/google/deluca/pull/57 from google:inverse_map baa4932444495538d91151653165cdcb386b52fc
cla: yesInternal change
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/google/deluca/pull/57 from google:inverse_map baa4932444495538d91151653165cdcb386b52fc
cla: yesHello! 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!
[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 DeviceArray
s if instantiated.)
[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 DeviceArray
s if instantiated.)
Internal change
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/google/deluca/pull/57 from google:inverse_map baa4932444495538d91151653165cdcb386b52fc
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
Please see https://readthedocs.org/projects/deluca for details about this release.
Source code(tar.gz)Please see https://readthedocs.org/projects/deluca for details about this release.
Source code(tar.gz)Please see https://readthedocs.org/projects/deluca for details about this release.
Source code(tar.gz)Please see https://readthedocs.org/projects/deluca for details about this release.
Source code(tar.gz)Please see https://readthedocs.org/projects/deluca for details about this release.
Source code(tar.gz)Please see https://readthedocs.org/projects/deluca for details about this release.
Source code(tar.gz)Dirty Pixels: Towards End-to-End Image Processing and Perception This repository contains the code for the paper Dirty Pixels: Towards End-to-End Imag
A face dataset generator with out-of-focus blur detection and dynamic interval adjustment.
MuseMorphose This repository contains the official implementation of the following paper: Shih-Lun Wu, Yi-Hsuan Yang MuseMorphose: Full-Song and Fine-
merged_depth runs (1) AdaBins, (2) DiverseDepth, (3) MiDaS, (4) SGDepth, and (5) Monodepth2, and calculates a weighted-average per-pixel absolute dept
PyTCI A toolbox that estimates the integration window of a sensory response using the "Temporal Context Invariance" paradigm (TCI). The TCI method Int
Video Matting via Consistency-Regularized Graph Neural Networks Project Page | Real Data | Paper Installation Our code has been tested on Python 3.7,
Python inverse kinematics for your robot model based on Pinocchio.
PianoGPT ai that generate music try it here https://share.streamlit.io/annasajkh/pianogpt/main/main.py or here https://huggingface.co/spaces/Annas/Pia
Pretrained-Pix2Seq We provide the pre-trained model of Pix2Seq. This version contains new data augmentation. The model is trained for 300 epochs and c
Dynamic-Vision-Transformer (Pytorch) This repo contains the official code and pre-trained models for the Dynamic Vision Transformer (DVT). Not All Ima
style-transfer-pytorch An implementation of neural style transfer (A Neural Algorithm of Artistic Style) in PyTorch, supporting CPUs and Nvidia GPUs.
Balanced MSE Code for the paper: Balanced MSE for Imbalanced Visual Regression Jiawei Ren, Mingyuan Zhang, Cunjun Yu, Ziwei Liu CVPR 2022 (Oral) News
WeakVRD-Captioning - Implementation of paper Improving Image Captioning with Better Use of Caption
Dressing in Order (DiOr) 👚 [Paper] 👖 [Webpage] 👗 [Running this code] The official implementation of "Dressing in Order: Recurrent Person Image Gene
Graph Optimizer This repo contains the official implementation of our CVPR 2021 paper - Hybrid Rotation Averaging: A Fast and Robust Rotation Averagin
sar_vessel_detect Code for the AI2 Skylight team's submission in the xView3 competition (https://iuu.xview.us) for vessel detection in Sentinel-1 SAR
cyclegan-demo 基于Pytorch构建CycleGAN示例 如何运行 准备数据集 将数据集整理成4个文件,分别命名为 trainA, trainB:训练集,A、B代表两类图片 testA, testB:测试集,A、B代表两类图片 例如 D:\CODE\CYCLEGAN-DEMO\DATA
TuckER: Tensor Factorization for Knowledge Graph Completion This codebase contains PyTorch implementation of the paper: TuckER: Tensor Factorization f
FairMOT - A simple baseline for one-shot multi-object tracking
Robust Video Matting (RVM) English | 中文 Official repository for the paper Robust High-Resolution Video Matting with Temporal Guidance. RVM is specific