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
delucaPerformant, differentiable reinforcement learning
delucaHi.
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 DeviceArrays 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 DeviceArrays 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)NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video Project Page | Paper NeuralRecon: Real-Time Coherent 3D Reconstruction from Mon
SmallInitEmb LayerNorm(SmallInit(Embedding)) in a Transformer I find that when t
Compositional-Attention This repository contains the official implementation for the paper Compositional Attention: Disentangling Search and Retrieval
Lie Transformer - Pytorch (wip) Implementation of Lie Transformer, Equivariant Self-Attention, in Pytorch. Only the SE3 version will be present in thi
PyCIL: A Python Toolbox for Class-Incremental Learning Introduction • Methods Reproduced • Reproduced Results • How To Use • License • Acknowledgement
Streamlit-Projects Streamlit projects available in this repo: An introduction to Streamlit presented at DS-OX (Feb 26, 2020) meetup Streamlit 101 - Ja
AYOLOv2 The main goal of this repository is to rewrite the object detection pipeline with a better code structure for better portability and adaptabil
xtbreak estimating and testing for many known and unknown structural breaks in time series and panel data. For an overview of xtbreak test see xtbreak
The Python3 import playground I have been confused about python modules and packages, this text tries to clear the topic up a bit. Sources: https://ch
BOVText: A Large-Scale, Bilingual Open World Dataset for Video Text Spotting Updated on December 10, 2021 (Release all dataset(2021 videos)) Updated o
Girlfriend Text Send text to girlfriend (or really anyone with a phone number) in the morning 1. Configure your settings in utils.py. phone_number = "
Cross View Transformers This repository contains the source code and data for our paper: Cross-view Transformers for real-time Map-view Semantic Segme
gym-mtsim: OpenAI Gym - MetaTrader 5 Simulator MtSim is a simulator for the MetaTrader 5 trading platform alongside an OpenAI Gym environment for rein
Differentiable Model Compression via Pseudo Quantization Noise DiffQ performs differentiable quantization using pseudo quantization noise. It can auto
arcface-Paddle 基于Paddle框架的arcface复现 ArcFace-Paddle 本项目基于paddlepaddle框架复现ArcFace,并参加百度第三届论文复现赛,将在2021年5月15日比赛完后提供AIStudio链接~敬请期待 参考项目: InsightFace Padd
ENN Incubator Collection of in-progress libraries for entity neural networks: Neural Network Architectures for Structured State Entity Gym: Abstractio
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking We revisit and address issues with Oxford 5k and Paris 6k image retrieval benchm
This repo holds the code for our weak supervision framework, ASTRA, described in our NAACL 2021 paper: "Self-Training with Weak Supervision"
Manifold Mixup Unofficial implementation of ManifoldMixup (Proceedings of ICML 19) for fast.ai (V2) based on Shivam Saboo's pytorch implementation of
functional-zoo Model definitions and pretrained weights for PyTorch and Tensorflow PyTorch, unlike lua torch, has autograd in it's core, so using modu