Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch

Code for RL experiments in "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

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Benchmark present methods for efficient reinforcement learning. Methods include Reptile, MAML, Residual Policy, etc. RL algorithms include DDPG, PPO.

Implementation of Model-Agnostic Meta-Learning (MAML) applied on Reinforcement Learning problems in TensorFlow 2.

Study of paper "Meta reinforcement learning for sim-to-real domain adaptation"

E-MAML, and RL-MAML baseline implemented in Tensorflow v1

元强化学习MAML实现, 修改了部分老旧而不能运行的代码, 并可以通过render直接查看训练的结果

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Re-implementation of MAML RL in several environment settings

Forked from https://github.com/tristandeleu/pytorch-maml-rl

Example RL code for paper "Multi-Step Model-Agnostic Meta-Learning: Convergence and Improved Algorithms"

Super simple implementation of MAML for RL in JAX

RL model for Adaptive Bitrate Streaming， with Pytorch， with and without MAML

Implementation of 'Model Agnostic Meta Learning' in pytorch

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Model Agnostic Meta Learning tested on Mixed Stock Prediction (MSP) and Free Pendulum environment (FPE) task

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MAML RL - Continual Learning course project

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MAML RLlib helpers

Implementation of Model-Agnostic Meta-Learning (MAML) for Reinforcement Learning using PyTorch and TorchRL.

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