Python Constrained-policy-gradient Resources

arXiv technical report soon available. we are updating the readme to be as comprehensive as possible Please ask any questions in Issues, thanks. Intro

GDAP Code for Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework that Works Environment Python (verified: v3.8) CUDA

django-permissions-policy Set the draft security HTTP header Permissions-Policy (previously Feature-Policy) on your Django app. Requirements Python 3.

Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods This repository is the official implementation of Seohong Park, Jaeky

Stochastic Gradient Trees - Python Stochastic Gradient Trees1 by Henry Gouk, Bernhard Pfahringer, and Eibe Frank implementation in Python. Based on th

CxGrad - Official PyTorch Implementation Contextual Gradient Scaling for Few-Shot Learning Sanghyuk Lee, Seunghyun Lee, and Byung Cheol Song In WACV 2

MEND: Model Editing Networks using Gradient Decomposition Setup Environment This codebase uses Python 3.7.9. Other versions may work as well. Create a

BoMb-OT Python3 implementation of the papers On Transportation of Mini-batches: A Hierarchical Approach and Improving Mini-batch Optimal Transport via

offline-MBPO This repository contains the code of a version of model-based RL algorithm MBPO, which is modified to perform in offline RL settings Pape

V-MPO Simple code to demonstrate Deep Reinforcement Learning by using an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) in Pyt

Ranger-Deep-Learning-Optimizer Ranger - a synergistic optimizer combining RAdam (Rectified Adam) and LookAhead, and now GC (gradient centralization) i

(Subspace Constrained) Mean Shift Algorithms in Euclidean and/or Directional Product Spaces This repository contains Python3 code for the mean shift a

Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient This repository is the official PyTorch implementation of "Edge Rewiring Go

PyTorch implementation of Constrained Policy Optimization (CPO) This repository has a simple to understand and use implementation of CPO in PyTorch. A

ttopt Description Gradient-free global optimization algorithm for multidimensional functions based on the low rank tensor train (TT) format and maximu

ProMP: Proximal Meta-Policy Search Implementations corresponding to ProMP (Rothfuss et al., 2018). Overall this repository consists of two branches: m

CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search This repository is the official implementation of CAPITAL: Optimal Subgrou

BasicRL: easy and fundamental codes for deep reinforcement learning BasicRL is an improvement on rainbow-is-all-you-need and OpenAI Spinning Up. It is

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allo

Simple Python tool that generates a pseudo-random password with numbers, letters, and special characters in accordance with password policy best practices.

Source code for the paper "Gradient Step Denoiser for convergent Plug-and-Play"

Stochastic Training is Not Necessary for Generalization -- Training competitive vision models without stochasticity This repository implements trainin

UPDeT Official Implementation of UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers (ICLR 2021 spotlight) The

Wonk is a tool for combining a set of AWS policy files into smaller compiled policy sets.

Convolutional Neural Network Visualizations This repository contains a number of convolutional neural network visualization techniques implemented in

model_based_energy_constrained_compression Code for paper "Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and

SpyTorch A tutorial on surrogate gradient learning in spiking neural networks Version: 0.4 This repository contains tutorial files to get you started

Intro PyTorch implementation of Learning to learn by gradient descent by gradient descent. Run python main.py TODO Initial implementation Toy data LST

PPO Pytorch C++ This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment t

Pytorch-DPPO Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286 Using PPO with clip loss (from https

Intro PyTorch implementation of Learning to learn by gradient descent by gradient descent. Run python main.py TODO Initial implementation Toy data LST

DEPRECATION This repository is deprecated and is no longer maintaned. Please see a more recent implementation of RL for continuous control at jax-sac.

This is the official implementation of the following paper: Torsten Scholak, Nathan Schucher, Dzmitry Bahdanau. PICARD - Parsing Incrementally for Con

The flowchart from the AWS IAM policy evaluation documentation page, as of 2021-09-12, and dating back to at least 2018-12-27, is the following: The f

PPO is a very popular Reinforcement Learning algorithm at present. OpenAI takes PPO as the current baseline algorithm. We use the PPO algorithm to train a policy to give the best action in any situation.

Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

Text-AutoAugment (TAA) This repository contains the code for our paper Text AutoAugment: Learning Compositional Augmentation Policy for Text Classific

rl: An efficient framework for reinforcement learning Requirements Introduction PPO Test Requirements name version Python =3.7 numpy =1.19 torch =1

[MM'21] Constrained Graphic Layout Generation via Latent Optimization This repository provides the official code for the paper "Constrained Graphic La

Pytorch-DPPO Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286 Using PPO with clip loss (from https

pytorch-a2c-ppo-acktr Update (April 12th, 2021) PPO is great, but Soft Actor Critic can be better for many continuous control tasks. Please check out

django-permissions-policy Set the draft security HTTP header Permissions-Policy (previously Feature-Policy) on your Django app. Requirements Python 3.

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks This is a Python3 / Pytorch implementation of TadGAN paper. The associated

Off-Policy Multi-Agent Reinforcement Learning (MARL) Algorithms This repository contains implementations of various off-policy multi-agent reinforceme

Gradient Episodic Memory for Continual Learning Source code for the paper: @inproceedings{GradientEpisodicMemory, title={Gradient Episodic Memory

Noisy Natural Gradient as Variational Inference PyTorch implementation of Noisy Natural Gradient as Variational Inference. Requirements Python 3 Pytor

PyTorch implementation of TRPO Try my implementation of PPO (aka newer better variant of TRPO), unless you need to you TRPO for some specific reasons.

Proximal Backpropagation Proximal Backpropagation (ProxProp) is a neural network training algorithm that takes implicit instead of explicit gradient s

Integrated Gradients This is the pytorch implementation of "Axiomatic Attribution for Deep Networks". The original tensorflow version could be found h

This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment to test the algorithm

Bonsai is a wrapper for the XGBoost and Catboost model training pipelines that leverages Bayesian optimization for computationally efficient hyperparameter tuning.

Gradient Matching for Domain Generalisation This is the official PyTorch implementation of Gradient Matching for Domain Generalisation. In our paper,

MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees. MooGBT optimizes for multiple objectives by defining constraints on sub-objective(s) along with a primary objective. The constraints are defined as upper bounds on sub-objective loss function. MooGBT uses a Augmented Lagrangian(AL) based constrained optimization framework with Gradient Boosted Trees, to optimize for multiple objectives.

CoSA is a scheduler for spatial DNN accelerators that generate high-performance schedules in one shot using mixed integer programming

[PDF] | [Slides] The official implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021 Long talk) Installation Inst

Batch Soft-DTW(Dynamic Time Warping) in TensorFlow2 including forward and backward computation Custom TensorFlow2 implementations of forward and backw

Toy implementations of some popular ML optimizers using Python/JAX

On the model-based stochastic value gradient for continuous reinforcement learning This repository is by Brandon Amos, Samuel Stanton, Denis Yarats, a

PGBM Probabilistic Gradient Boosting Machines (PGBM) is a probabilistic gradient boosting framework in Python based on PyTorch/Numba, developed by Air

You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient (paper) @misc{zhang2021compress,

Geometry-Aware Gradient Algorithms for Neural Architecture Search This repository contains the code required to run the experiments for the DARTS sear

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models and supports classification, regression and ranking. TF-DF is a TensorFlow wrapper around the Yggdrasil Decision Forests C++ libraries. Models trained with TF-DF are compatible with Yggdrasil Decision Forests' models, and vice versa.

Gradient-Induced Co-Saliency Detection Zhao Zhang*, Wenda Jin*, Jun Xu, Ming-Ming Cheng ⭐ Project Home » The official repo of the ECCV 2020 paper Grad

Paddle-RLBooks Welcome to Paddle-RLBooks which is a reinforcement learning code study guide based on pure PaddlePaddle. 欢迎来到Paddle-RLBooks，该仓库主要是针对强化学

Reinforcement Learning (PyTorch) 🤖 + 🍰 = ❤️ This repo will contain PyTorch implementation of various fundamental RL algorithms. It's aimed at making

A Closer Look at Invalid Action Masking in Policy Gradient Algorithms This repo contains the source code to reproduce the results in the paper A Close

Text Generation by Learning from Demonstrations The README was last updated on March 7, 2021. The repo is based on fairseq (v0.9.?). Paper arXiv Prere

BNN - BN = ? Training Binary Neural Networks without Batch Normalization Codes for this paper BNN - BN = ? Training Binary Neural Networks without Bat

✅ Access Analyzer - Batch Policy Validator This script will analyze using AWS Access Analyzer - Policy Validation all your account customer managed IA

Multi-Agent-Deep-Deterministic-Policy-Gradients A Pytorch implementation of the multi agent deep deterministic policy gradients(MADDPG) algorithm This

⚡ OPAL ⚡ Open Policy Administration Layer OPAL is an administration layer for Open Policy Agent (OPA), detecting changes to both policy and policy dat

This is the original implementation of our paper, A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (arXiv:1706.1

A tour through tensorflow with financial data I present several models ranging in complexity from simple regression to LSTM and policy networks. The s

Stock Trading Market OpenAI Gym Environment with Deep Reinforcement Learning using Keras Overview This project provides a general environment for stoc

...Minimizing the mean square error on future experience. - Richard S. Sutton BTGym Scalable event-driven RL-friendly backtesting library. Build on

Markov Decision Process A Markov decision process (MDP), by definition, is a sequential decision problem for a fully observable, stochastic environmen

gHHC Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, D

GS-WGAN This repository contains the implementation for GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators (NeurIPS

PGPElib A mini library for Policy Gradients with Parameter-based Exploration [1] and friends. This library serves as a clean re-implementation of the

RPG (Reward-Randomized Policy Gradient) Zhenggang Tang*, Chao Yu*, Boyuan Chen, Huazhe Xu, Xiaolong Wang, Fei Fang, Simon Shaolei Du, Yu Wang, Yi Wu (

aws-allowlister Automatically compile an AWS Service Control Policy that ONLY allows AWS services that are compliant with your preferred compliance fr

NLopt is a library for nonlinear local and global optimization, for functions with and without gradient information. It is designed as a simple, unifi

Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allo

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

eXtreme Gradient Boosting Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting l

Master status: Development status: Package information: TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assista

Gradient Centralization TensorFlow This Python package implements Gradient Centralization in TensorFlow, a simple and effective optimization technique

DRLib：A concise deep reinforcement learning library, integrating HER and PER for almost off policy RL algos A concise deep reinforcement learning libr

Storchastic is a PyTorch library for stochastic gradient estimation in Deep Learning

PyTorch implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping Paper: https://arxiv.org/abs/2102.06171.pdf Original code: htt

Keras implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

eXtreme Gradient Boosting Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting l

Differentiable Morphing Image morphing without reference points by applying warp maps and optimizing over them. Differentiable Morphing is machine lea

SLM Lab Modular Deep Reinforcement Learning framework in PyTorch. Documentation: https://slm-lab.gitbook.io/slm-lab/ BeamRider Breakout KungFuMaster M

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a