Bootstrapped Meta-Learning Replication

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A2C Baseline	Meta-Gradient Reinforcement Learning	Bootstrapped Meta-Learning

How To Use

A2C

$ python main.py --algo A2C --seed 0 --epsilon-EN 3e-1 --lr 1e-1 --gamma 0.99 --log-dir "logs" --max-steps 6400000

Both SGD and Adam are supported, however, it is not encouraged to use Adam otherwise you won't see desired result.

MGRL

$ python main.py --algo MGRL --seed 0 --lr 1e-1 --meta-lr 1e-4 -epsilon-meta 0.12 --gamma 0.99 --T 15 --max-steps 6400000

BMG

$ python main.py --algo BMG -seed 0 --lr 1e-1 --meta-lr 1e-4 --gamma 0.99 --K 7 --L 9 --max-steps 6400000

We use 7 and 9 corresponding to K and L for default, for details, you can redirect to the original paper Bootstrapped Meta-Learning

Visualization

python plot.py --algo BMG

The results will be stored in results folder by default. To plot, please specify algorithm name that corresponds to the latest run.

Requirements

$ pip install -r requirements.txt