This repository contains code used for the experiments reported in section 8.5 of Experience Selection in Deep Reinforcement Learning for Control. The code is forked from the openAI baselines repository, the readme of which is given below. The changes are limited to the DDPG algorithm and include:
- Added experience replay code that allows experience retention based on TDE, exploration or reservoir sampling in addition to FIFO.
- Added the option for exploration decay when using parameter noise
- Added helper functions to perform the experiments reported in the paper.
- Changed from the mujoco based continuous control benchmarks to the open source Roboschool alternatives.
OpenAI Baselines is a set of high-quality implementations of reinforcement learning algorithms.
These algorithms will make it easier for the research community to replicate, refine, and identify new ideas, and will create good baselines to build research on top of. Our DQN implementation and its variants are roughly on par with the scores in published papers. We expect they will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones.
You can install it by typing:
git clone https://github.com/openai/baselines.git
cd baselines
pip install -e .To cite this repository in publications:
@misc{baselines,
author = {Dhariwal, Prafulla and Hesse, Christopher and Klimov, Oleg and Nichol, Alex and Plappert, Matthias and Radford, Alec and Schulman, John and Sidor, Szymon and Wu, Yuhuai},
title = {OpenAI Baselines},
year = {2017},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/openai/baselines}},
}