131 Open source projects that are alternatives of or similar to Relational_Deep_Reinforcement_Learning

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).

Variants of Vision Transformer and its downstream tasks

Deep Reinforcement Learning by using Proximal Policy Optimization and Random Network Distillation in Tensorflow 2 and Pytorch with some explanation

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

Proximal Policy Optimization(PPO) with Intrinsic Curiosity Module(ICM)

PyTorch implementation of some reinforcement learning algorithms: A2C, PPO, Behavioral Cloning from Observation (BCO), GAIL.

Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020

Machine Learning Finite State Machine Models from Data with Genetic Algorithms

Python library for Ceteris Paribus Plots (What-if plots)

Source code for AAAI 2019 paper "Relation Structure-Aware Heterogeneous Information Network Embedding"

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

Transformer/Transformer-XL/R-Transformer examples and explanations

PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.

Code for the paper "MASTER: Multi-Aspect Non-local Network for Scene Text Recognition" (Pattern Recognition 2021)

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

Sequence to Sequence Models in PyTorch

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

[TPAMI Under Submission] Generative Multi-Label Zero-Shot Learning

a unified environment for supervised learning and reinforcement learning in the context of quantitative trading

🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!

Examples of Data Science projects and Artificial Intelligence use cases

Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)

Trading Environment(OpenAI Gym) + PPO(TensorForce)

Molecular Explanation Generator

☔ Deep RL agents with PyTorch☔

ROS 2 enabled Machine Learning algorithms

Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

LONG-TERM SERIES FORECASTING WITH QUERYSELECTOR – EFFICIENT MODEL OF SPARSEATTENTION

Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

Provide full reinforcement learning benchmark on mujoco environments, including ddpg, sac, td3, pg, a2c, ppo, library

No description or website provided.

PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

MindsDB server allows you to consume and expose MindsDB workflows, through http.

PyTorch code for NeurIPSW 2020 paper (4th Workshop on Meta-Learning) "Few-Shot Unsupervised Continual Learning through Meta-Examples"

[IEEE TNSRE] "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"

强化学习中文教程，在线阅读地址：https://datawhalechina.github.io/easy-rl/

Essential NLP & ML, short & fast pure Python code

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

An elegant PyTorch deep reinforcement learning library.

Predict materials properties using only the composition information!

Modularized Implementation of Deep RL Algorithms in PyTorch

Visualize BERT's self-attention layers on text classification tasks

31 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

Implementation of the paper "Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning"

Lightweight deep RL Libraray for continuous control.

Reinforcement learning models in ViZDoom environment

This Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.

Tensorflow implementation of Generative Adversarial Imitation Learning(GAIL) with discrete action

Live Trading. Please star.

A PyTorch implementation of the Light Temporal Attention Encoder (L-TAE) for satellite image time series. classification

⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.

DeepRL algorithms implementation easy for understanding and reading with Pytorch and Tensorflow 2(DQN, REINFORCE, VPG, A2C, TRPO, PPO, DDPG, TD3, SAC)

A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.

Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs