Toy meta-RL environments for testing algorithms implementations
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Updated
Feb 21, 2024 - Python
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"
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.
GenReL-World is a general Reinforcement Learning framework to utilize various world models as environments for robot manipulation
Repo to reproduce the First-Explore paper results
My notes on reinforcement learning papers
A curated list of awesome Meta Reinforcement Learning
🌈 The code and methods offered in Awesome-META+: https://wangjingyao07.github.io/Awesome-Meta-Learning-Platform/
Learning to reinforcement learn for Neural Architecture Search
Implementation of Model-Agnostic Meta-Learning (MAML) applied on Reinforcement Learning problems in TensorFlow 2.
Implementation of our paper "Meta Reinforcement Learning with Task Embedding and Shared Policy"
Code for FOCAL Paper Published at ICLR 2021
Re-implementations of SOTA RL algorithms.
"모두를 위한 메타러닝" 책에 대한 코드 저장소
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
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