Implementation of Meta-SGD applied on Reinforcement Learning problems in Pytorch. This repository includes environments introduced in (Duan et al., 2016, Finn et al., 2017): multi-armed bandits, tabular MDPs, continuous control with MuJoCo, and 2D navigation task.
You can use the main.py script in order to run reinforcement learning experiments with Meta-SGD. This script was tested with Python 3.7.
python main.py --env-name HalfCheetahDir-v1 --fast-lr 0.1 --lr-ppo 5e-5 --num-workers 20 --fast-batch-size 20 --meta-batch-size 20 --num-layers 2 --hidden-size 100 --num-batches 1000 --tau 0.99 --ppo-update-time 5 --output-folder maml-halfcheetah-dir-ppo --device cuda
This repository are based on the paper
Li Z, Zhou F, Chen F, et al. Meta-SGD: Learning to learn quickly for few-shot learning[J]. arXiv preprint arXiv:1707.09835, 2017. [ArXiv]