A PyTorch Library for Meta-learning Research
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Updated
Jul 3, 2023 - 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.
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 for Variable Action Spaces"
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
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