proceduralia/randomist

Code for Policy Optimization as Online Learning with Mediator Feedback

Policy Optimization as Online Learning with Mediator Feedback

@inproceedings{randomist,
  author    = {Alberto Maria Metelli and
               Matteo Papini and
               Pierluca D'Oro and
               Marcello Restelli},
  title     = {Policy Optimization as Online Learning with Mediator Feedback},
  booktitle = {The Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021}
}

Installation

We mainly use pytorch and numpy as linear algebra libraries. Run pip install -r requirements.txt, preferably on a clean environment, to install all the required packages.

Code structure

The structure of the repository is the following:

algorithms/
envs/
mellog/
utils.py
...

The algorithms/ directory contains scripts with classes for each algorithm. Some of them, are grouped together (OPTIMIST and FTL, PHE and the discrete version of RANDOMIST). algorithms/randomist.py and algorithms/discrete.py contain the abstract classes instantiated by the other algorithms to perform policy optimization with a discrete number of policies, with and without importance sampling.

How to run experiments

Experiments can be run with three main scripts:

• run_my_experiment.py for the illustrative experiments in the MAB setting;
• run_lqg_experiment.py for experiments in the Linear Quadratic Gaussian Regulator environment:
• run_rl_experiment.py for experiments in the Mountain Car and Cartpole environments.

Each script requires the specification of a log directory and a name for the experiment. The last one should be the same for different runs of the same experiment. For each run, a directory is produced, contained files describing the used hyperparameters and a metrics.csv file with the logged metrics. For instance, for running MCMC-RANDOMIST on the Mountain Car environment for 2500 iterations, you can launch:

python run_rl_experiment.py --algorithm randomistMCMC --logdir log/mountaincar --exp_name mcmcrandomist --logging_freq 25 --pseudo_rewards_per_timestep 1.1 --env car --n_iterations 2500

To reproduce the results in the paper, we also provide three *.sh scripts that automate the process of launching multiple runs of the experiments using different algorithms. For instance, just use the command sh run_lqg_experiment.sh to reproduce the complete LQG experiment.