Skip to content
Learning to Adapt in Dynamic, Real-World Environment through Meta-Reinforcement Learning
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
docker docker config May 2, 2019
experiment_utils minimal code May 3, 2019
learning_to_adapt adding logging of the losses May 2, 2019
run_scripts adding run commands in readme May 6, 2019
viskit first commit May 1, 2019
README.md adding run commands in readme May 6, 2019
requirements.txt docker config May 2, 2019

README.md

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

Acknowledgements

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

You can’t perform that action at this time.