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Learning to Adapt in Dynamic, Real-World Environment through Meta-Reinforcement Learning

Implementation of Learning to Adapt in Dynamic, Real-World Environment through Meta-Reinforcement Learning. The code is written in Python 3 and builds on Tensorflow. The environments require the Mujoco131 physics engine.

Getting Started

A. Docker

If not installed yet, set up docker on your machine. Pull our docker container iclavera/learning_to_adapt from docker-hub:

docker pull iclavera/learning_to_adapt All the necessary dependencies are already installed inside the docker container.

B. Anaconda

Ensure that you have a working MPI implementation (see here for more instructions).

For Ubuntu you can install MPI through the package manager: sudo apt-get install libopenmpi-dev

If not done yet, install anaconda by following the instructions here

conda env create -f docker/environment.yml

For running the environments, the Mujoco physics engine version 131 is needed. For setting up Mujoco and mujoco-py

Usage

The run scripts are located in the folder run_scripts. In order to run experiments with GrBAL, run the following command:

python run_scripts/run_grbal.py

If instead, you want to run ReBAL:

python run_scripts/run_rebal.py

We have also implement a non-adaptive model-based method that uses random shooting or cross-entropy for planning. You can run this baseline by executing the command:

python run_scripts/run_mb_mpc.py

When running experiments, the data will be stored in data/$EXPERIMENT_NAME. You can visualize the learning process by using the visualization kit:

python viskit/frontend.py data/$EXPERIMENT_NAME

In order to visualize and test a learned policy run:

python experiment_utils/sim_policy data/$EXPERIMENT_NAME

Acknowledgements

This repository is partly based on Duan et al., 2016.

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