A PyTorch Library for Meta-learning Research
-
Updated
Jun 7, 2024 - Python
A PyTorch Library for Meta-learning Research
Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022
Re-implementations of SOTA RL algorithms.
Official Implementation for "In-Context Reinforcement Learning for Variable Action Spaces"
Code for FOCAL Paper Published at ICLR 2021
Implementation of our paper "Meta Reinforcement Learning with Task Embedding and Shared Policy"
Implementation of Model-Agnostic Meta-Learning (MAML) applied on Reinforcement Learning problems in TensorFlow 2.
Official Implementation for "In-Context Reinforcement Learning from Noise Distillation"
Implementation of BIMRL: Brain Inspired Meta Reinforcement Learning - Roozbeh Razavi et al. (IROS 2022)
🌈 The code and methods offered in Awesome-META+: https://wangjingyao07.github.io/Awesome-Meta-Learning-Platform/
Toy meta-RL environments for testing algorithms implementations
GenReL-World is a general Reinforcement Learning framework to utilize various world models as environments for robot manipulation
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."