Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

Last update: May 12, 2022

Overview

Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

Prerequisites:

Python3
Matplotlib
Numpy
Itertools
cvxpy

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Implementation for Explicable Reward Design for Reinforcement Learning Agents
- Code: Main Paper
  - ROOMSNAVENV
  - LINEKEYNAVENV
- Code: Appendix

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Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

Related tags

Overview

Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

Prerequisites:

Navigation

Owner

ComPhy: Compositional Physical Reasoning ofObjects and Events from Videos

Implementation of our paper 'RESA: Recurrent Feature-Shift Aggregator for Lane Detection' in AAAI2021.

Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.

This repository is all about spending some time the with the original problem posed by Minsky and Papert

Soomvaar is the repo which 🏩 contains different collection of 👨‍💻🚀code in Python and 💫✨Machine 👬🏼 learning algorithms📗📕 that is made during 📃 my practice and learning of ML and Python✨💥

Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

Scalable Multi-Agent Reinforcement Learning

Transfer-Learn is an open-source and well-documented library for Transfer Learning.

This is the accompanying toolbox for the paper "A Survey on GANs for Anomaly Detection"

This repo provides function call to track multi-objects in videos

Code accompanying the paper on "An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers" published at NeurIPS, 2021

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