Skip to content
No description, website, or topics provided.
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
experiments
libs
remps
.gitignore
.gitmodules
LICENSE Added LICENSE file Jun 4, 2019
README.md Add mail to readme Jun 8, 2019
requirements.txt
setup.py Refactoring, first version May 10, 2019

README.md

REMPS

Repository for the paper "Reinforcement Learning in Configurable Continuous Environments". Accepted at ICML 2019.

Introduction

A Conf-MDP is a MDP in which the transition function p: (s,a) -> s' is affected by some configurable parameters \omega. The effect of the parameter on the transition function can be known (exact case) or unknown (approximated case).

Documentation

The class `confMDP is the abstract class of the environment providing the method setParams for the setting of the parameters. An environment should implement this class.

Installation

Install dependencies inside requirements.txt

pip install -r requirements.txt

Using setup.py:

cd remps
pip install -e .

Run

REMPS on CartPole

Cartpole has env_id=1. Noise_std is useless in cartpole. Omega is the initial value of the environment parameter.

python ${ROOT}/remps/runExperiment.py --no-render \
                                        --train-model-policy \
                                        --no-restore-variables \
                                        --no-save-variables \
                                        --hidden-layer-size 0 \
                                        --iteration-number 10000 \
                                        --omega 8 \
                                        --reward-type 3 \
                                        --env-id 1 \
                                        --n-actions 2 \
                                        --n-trajectories 100 \
                                        --max-steps 1000 \
                                        --eval-freq 2 \
                                        --eval-steps 2 \
                                        --noise-std 1e-5  \
                                        --epsilon 2 \
                                        --use-remps \

REMPS on Chain

Add the epsilons you are interested in in runChain.

python runChain.py  --max-steps 500 \
                    --n_trajectories 10

REMPS on TORCS

Download the TORCS code:

git submodule update --init

The torcs code is inside the folder libs/gym_torcs. Install torcs following the steps listed here

Run the torcs experiment with:

./experiments/train_remps_torcs.sh

Authors

Citing

@InProceedings{pmlr-v97-metelli19a,
  title = 	 {Reinforcement Learning in Configurable Continuous Environments},
  author = 	 {Metelli, Alberto Maria and Ghelfi, Emanuele and Restelli, Marcello},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {4546--4555},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Long Beach, California, USA},
  month = 	 {09--15 Jun},
  publisher = 	 {PMLR},`
  pdf = 	 {http://proceedings.mlr.press/v97/metelli19a/metelli19a.pdf},
  url = 	 {http://proceedings.mlr.press/v97/metelli19a.html},
  abstract = 	 {Configurable Markov Decision Processes (Conf-MDPs) have been recently introduced as an extension of the usual MDP model to account for the possibility of configuring the environment to improve the agent’s performance. Currently, there is still no suitable algorithm to solve the learning problem for real-world Conf-MDPs. In this paper, we fill this gap by proposing a trust-region method, Relative Entropy Model Policy Search (REMPS), able to learn both the policy and the MDP configuration in continuous domains without requiring the knowledge of the true model of the environment. After introducing our approach and providing a finite-sample analysis, we empirically evaluate REMPS on both benchmark and realistic environments by comparing our results with those of the gradient methods.}
}
``
You can’t perform that action at this time.