Model-Based Reinforcement Learning with Gaussian Processes

Aidan Scannell
Mar 13, 2019
Code Follow

In this work I reimplemented the PILCO algorithm (originally written in MATLAB) in Python using Tensorflow and GPflow. This work was mainly carried out for personal development and some of the implementation is based on this Python implementation. This repository will mainly serve as a baseline for my future research.

I implemented the cart pole benchmark using MuJoCo and OpenAI. I did this because OpenAI’s CartPole environment does not have a continuous action space and because the InvertedPendulum-v2 environment uses an “inverted” cart pole. The new environment represents the traditional cart pole benchmark with a continuous action space.

The env/cart_pole_env.py file contains the new CartPole class, based on InvertedPendulum-v2. I also created the env/cart_pole.xml file defining the MuJoCo environment for the traditional cart pole.

gaussian-processes machine-learning python reinforcement-learning code
Aidan Scannell
Aidan Scannell
Postdoctoral Researcher

My research interests include model-based reinforcement learning, probabilistic machine learning (gaussian processes, Bayesian neural networks, approximate Bayesian inference, etc), learning-based control and optimal control.