A PyTorch Library for Meta-learning Research
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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
Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
"모두를 위한 메타러닝" 책에 대한 코드 저장소
Re-implementations of SOTA RL algorithms.
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.
🌈 The code and methods offered in Awesome-META+: https://wangjingyao07.github.io/Awesome-Meta-Learning-Platform/
Learning to reinforcement learn for Neural Architecture Search
A curated list of awesome Meta Reinforcement Learning
Repo to reproduce the First-Explore paper results
My notes on reinforcement learning papers
GenReL-World is a general Reinforcement Learning framework to utilize various world models as environments for robot manipulation
Toy meta-RL environments for testing algorithms implementations
Official Implementation for "In-Context Reinforcement Learning for Variable Action Spaces"
Implementation of BIMRL: Brain Inspired Meta Reinforcement Learning - Roozbeh Razavi et al. (IROS 2022)
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