rafaelsterzinger/pytorch-maml-rl

Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch

On the Robustness of Context- and Gradient-based Meta-Reinforcement Learning Algorithms

For installing MuJoCo refer here.

Setting the environment

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

Reproduce results

Parameter --type denotes the amount of quaters included during training.

Parameter --num-steps denotes the amount of gradient steps to take.

Training

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

Testing

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