mike-gimelfarb / bayesian-epsilon-greedy

Public repository for a paper in UAI 2019 describing adaptive epsilon-greedy exploration using Bayesian ensembles for deep reinforcement learning.

Adaptive Epsilon-Greedy Exploration Policy using Bayesian Ensembles

Description

This small and fairly self-contained (see prerequisites below) package accompanies an article published in Uncertainty in Artificial Intelligence (UAI 2019) entitled "ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning"

It contains an implementation of an adaptive epsilon-greedy exploration policy that adapts the exploration parameter from data in model-free reinforcement learning.

Prerequisites

Tested on Python 3.5 with standard packages (e.g. numpy, scipy, abc) and the following additional packages:

1. Keras with tensorflow backend
2. OpenAI Gym for the Cartpole implementation

Citation

To cite the framework:

@inproceedings{GimelfarbSL19,
author={Michael Gimelfarb and Scott Sanner and Chi{-}Guhn Lee},
editor={Amir Globerson and Ricardo Silva},
title={Epsilon-BMC: {A} Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning},
booktitle={Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, {UAI} 2019, Tel Aviv, Israel, July 22-25, 2019},
pages={162},
publisher={{AUAI} Press},
year={2019},
url={http://auai.org/uai2019/proceedings/papers/162.pdf} }