openai / Mlsh
Code for the paper "Meta-Learning Shared Hierarchies"
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Status: Archive (code is provided as-is, no updates expected)
Meta-Learning Shared Hierarchies
Code for Meta-Learning Shared Hierarchies.
Installation
Add to your .bash_profile (replace ... with path to directory):
export PYTHONPATH=$PYTHONPATH:/.../mlsh/gym;
export PYTHONPATH=$PYTHONPATH:/.../mlsh/rl-algs;
Install MovementBandits environments:
cd test_envs
pip install -e .
Running Experiments
python main.py --task AntBandits-v1 --num_subs 2 --macro_duration 1000 --num_rollouts 2000 --warmup_time 20 --train_time 30 --replay False AntAgent
Once you've trained your agent, view it by running:
python main.py [...] --replay True --continue_iter [your iteration] AntAgent
The MLSH script works on any Gym environment that implements the randomizeCorrect() function. See the envs/ folder for examples of such environments.
To run on multiple cores:
mpirun -np 12 python main.py ...
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