TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
-
Updated
Jun 4, 2024 - Python
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning
GenReL-World is a general Reinforcement Learning framework to utilize various world models as environments for robot manipulation
"모두를 위한 메타러닝" 책에 대한 코드 저장소
🌈 The code and methods offered in Awesome-META+: https://wangjingyao07.github.io/Awesome-Meta-Learning-Platform/
Toy meta-RL environments for testing algorithms implementations
Official Implementation for "In-Context Reinforcement Learning for Variable Action Spaces"
Official Implementation for "In-Context Reinforcement Learning from Noise Distillation"
Code for FOCAL Paper Published at ICLR 2021
Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022
Re-implementations of SOTA RL algorithms.
Repo to reproduce the First-Explore paper results
A PyTorch Library for Meta-learning Research
An implementation of Meta RL submitted as a course project for the course EE675A (Introduction to Reinforcement Learning)
Implementation of BIMRL: Brain Inspired Meta Reinforcement Learning - Roozbeh Razavi et al. (IROS 2022)
This repo contains the implementation of some new papers on some advanced topics of machine learning e.g. meta-learning, reinforcement-learning, meta-reinforcement-learning, continual-learning and etc.
Implementation of Model-Agnostic Meta-Learning (MAML) applied on Reinforcement Learning problems in TensorFlow 2.
Learning to reinforcement learn for Neural Architecture Search
A curated list of awesome Meta Reinforcement Learning
Add a description, image, and links to the meta-rl topic page so that developers can more easily learn about it.
To associate your repository with the meta-rl topic, visit your repo's landing page and select "manage topics."