Python Soft-actor-critic Resources

Bailando Code for CVPR 2022 (oral) paper "Bailando: 3D dance generation via Actor-Critic GPT with Choreographic Memory" [Paper] | [Project Page] | [Vi

CPDeform Code and data for paper Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics at ICLR 2022 (Spotlight). @InProceed

Compositional Soft Prompting (CSP) Compositional soft prompting (CSP), a parameter-efficient learning technique to improve the zero-shot compositional

Graph Data Science: an abstraction layer in Python for building knowledge graphs, integrated with popular graph libraries – atop Pandas, RDFlib, pySHACL, RAPIDS, NetworkX, iGraph, PyVis, pslpython, pyarrow, etc.

ATAC: Adversarially Trained Actor Critic Adversarially Trained Actor Critic for Offline Reinforcement Learning by Ching-An Cheng*, Tengyang Xie*, Nan

Decoupled Smoothing in Probabilistic Soft Logic Experiments for "Decoupled Smoothing in Probabilistic Soft Logic". Probabilistic Soft Logic Probabilis

Differentiable Simulation of Soft Multi-body Systems Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin [Paper] [Code] Updates The C++ backend s

SNCSE SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples This is the repository for SNCSE. SNCSE aims to allev

Evolution Gym A large-scale benchmark for co-optimizing the design and control of soft robots. As seen in Evolution Gym: A Large-Scale Benchmark for E

Policy Gradient Algorithms From Scratch (NumPy) This repository showcases two policy gradient algorithms (One Step Actor Critic and Proximal Policy Op

Multi-task Multi-agent Soft Actor Critic for SMAC Overview The CARE formulti-task: Multi-Task Reinforcement Learning with Context-based Representation

Computational Design and Dynamics of Soft Systems · This is a repository that contains the source code for generating the lecture notes, handouts, exe

This repository is no longer maintained. Please use our new Softlearning package instead. Soft Actor-Critic Soft actor-critic is a deep reinforcement

SA-tensorflow Tensorflow implementation of soft-attention mechanism for video caption generation. An example of soft-attention mechanism. The attentio

🌀 Pykka Pykka makes it easier to build concurrent applications. Pykka is a Python implementation of the actor model. The actor model introduces some

Advantage Actor Critic (A2C): jax + flax implementation Current version supports only environments with continious action spaces and was tested on muj

Thespian Actor Library This library provides the framework of an Actor model for use by applications implementing Actors. Thespian Site with Documenta

Soft Serve monitor Soft Sevre is a very nice git server. It offers a really nice TUI to browse the repositories on the server. Unfortunately, it does

ChainerRL and PFRL ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement al

ID-Unet: Iterative-view-synthesis(CVPR2021 Oral) Tensorflow implementation of ID-Unet: Iterative Soft and Hard Deformation for View Synthesis. Overvie

Movie-Success-Prediction A simple python program which predicts the success of a movie based on it's type, actor, actress and director. The program us

Soft-Exponential-Activation-Function: Implementation of parameterized soft-exponential activation function. In this implementation, the parameters are

Annex Bubt Scraping Script I think this is the first public repository that provides free annex-BUBT, BUBT-Soft, and BUBT website scraping API script

RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (Paper) (Slides) (Video) RuleBERT is a pre-trained language model that has been fine-tune

HiFi-GAN An 16kHz implementation of HiFi-GAN for soft-vc. Relevant links: Official HiFi-GAN repo HiFi-GAN paper Soft-VC repo Soft-VC paper Example Usa

Residual Pathway Priors for Soft Equivariance Constraints This repo contains the implementation and the experiments for the paper Residual Pathway Pri

Auto-DrAC: Automatic Data-Regularized Actor-Critic This is a PyTorch implementation of the methods proposed in Automatic Data Augmentation for General

Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning Official implementation of ACC, described in the paper "Adaptively Calibrated C

Softlearning Softlearning is a deep reinforcement learning toolbox for training maximum entropy policies in continuous domains. The implementation is

This repository is the code for NeurIPS 2021 submission "A Surrogate Objective Framework for Prediction+Programming with Soft Constraints". Edit 2021/

Resilient projection-based consensus actor-critic (RPBCAC) algorithm We implement the RPBCAC algorithm with nonlinear approximation from [1] and focus

Balayya-Bot Reddit comment bot emulating Telugu actor N. Bala Krishna. Project structure config.py contains Bot's higher level configuration. generate

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

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

SOFT: Softmax-free Transformer with Linear Complexity SOFT: Softmax-free Transformer with Linear Complexity, Jiachen Lu, Jinghan Yao, Junge Zhang, Xia

ACTOR Official Pytorch implementation of the paper "Action-Conditioned 3D Human Motion Synthesis with Transformer VAE", ICCV 2021. Please visit our we

Permute Me Softly: Learning Soft Permutations for Graph Representations

Fast Soft Color Segmentation

Soft-Decision-Tree Soft-Decision-Tree is the pytorch implementation of Distilling a Neural Network Into a Soft Decision Tree, paper recently published

Advantage async actor-critic Algorithms (A3C) in PyTorch @inproceedings{mnih2016asynchronous, title={Asynchronous methods for deep reinforcement lea

Deep Reinforcement Learning with pytorch & visdom Sample testings of trained agents (DQN on Breakout, A3C on Pong, DoubleDQN on CartPole, continuous A

Asynchronous Advantage Actor-Critic in PyTorch This is PyTorch implementation of A3C as described in Asynchronous Methods for Deep Reinforcement Learn

LM-Critic: Language Models for Unsupervised Grammatical Error Correction This repo provides the source code & data of our paper: LM-Critic: Language M

NEWLY ADDED A3G A NEW GPU/CPU ARCHITECTURE OF A3C FOR SUBSTANTIALLY ACCELERATED TRAINING!! RL A3C Pytorch NEWLY ADDED A3G!! New implementation of A3C

DC-ShadowNet Introduction This is an implementation of the following paper DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised

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

DSP Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation". Accepted by ACM Multimedia 2021. Authors

The Power of Scale for Parameter-Efficient Prompt Tuning Implementation of soft embeddings from https://arxiv.org/abs/2104.08691v1 using Pytorch and H

pytorch-a3c-mujoco Disclaimer: my implementation right now is unstable (you ca refer to the learning curve below), I'm not sure if it's my problems. A

Lightweight, efficient and stable implementations of deep reinforcement learning algorithms using PyTorch. 🔥

Soft DTW Loss Function for PyTorch in CUDA This is a Pytorch Implementation of Soft-DTW: a Differentiable Loss Function for Time-Series which is batch

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

Optimal Model Design for Reinforcement Learning This repository contains JAX code for the paper Control-Oriented Model-Based Reinforcement Learning wi

PAWS-TF 🐾 Implementation of Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples (PAWS)

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

Bucatini: a soft PIPE PHY for FPGA SerDes Bucatini is a noodly gateware layer capable of transforming an FPGA SerDes into a PIPE PHY, allowing you to

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

Proto-RL: Reinforcement Learning with Prototypical Representations This is a PyTorch implementation of Proto-RL from Reinforcement Learning with Proto

Deep-Reinforcement-Learning-in-Stock-Trading Using deep actor-critic model to learn best strategies in pair trading Abstract Partially observed Markov

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

Tsinghua Dogs classification with Deep Metric Learning 1. Introduction Tsinghua Dogs dataset Tsinghua Dogs is a fine-grained classification dataset fo

ChainerRL ChainerRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Ch