For installing MuJoCo refer here.
Create a virtual environment, activate it and install the requirements in requirements.txt
.
virtualenv venv --python=python3.7
source venv/bin/activate
pip install -r requirements.txt
Parameter --type denotes the amount of quaters included during training.
Parameter --num-steps denotes the amount of gradient steps to take.
You can use the train.py
script in order to run reinforcement learning experiments with MAML. Note that by default, logs are available in train.py
but are not saved (eg. the returns during meta-training). For example, to run the script on HalfCheetah-Vel:
python train.py --config configs/maml/ant-goal.yaml --output-folder maml-ant-goal --seed 1 --num-workers 4 --type 2
Once you have meta-trained the policy, you can test it on the same environment using test.py
:
python test.py --config maml-ant-goal/config.json --policy maml-ant-goal/policy.th --output maml-ant-goal/results.npz --seed 1 --meta-batch-size 20 --num-batches 10 --num-workers 4 --num-steps 10