rssalessio / ModelFreeActiveExplorationRL

This repository hosts the code accompanying the paper "Model-Free Active Exploration in Reinforcement Learning". Our study approaches the exploration problem in Reinforcement Learning (RL) from an information-theoretical viewpoint and presents a novel, efficient, and entirely model-free solution.

Model Free Active Exploration in Reinforcement Learning

This repository hosts the code accompanying the paper "Model-Free Active Exploration in Reinforcement Learning", published at NeurIPS 2023. Our study approaches the exploration problem in Reinforcement Learning (RL) from an information-theoretical viewpoint and presents a novel, efficient, and entirely model-free solution.

Authors: Alessio Russo, Alexandre Proutiere
Code author: Alessio Russo
License: MIT
Additional license info: the CartPoleSwingUp environment, in CartPoleSwingUp/env, was originally taken from the BSuite repository (DeepMind Technologies Limited). The files in that directory were originally licensed under the APACHE 2.0 license, which can be found at the root of this repository. Changes and additions to those files are licensed under MIT.

Requirements

To run the examples you need atleast Python 3.10 and the following libraries installed: numpy scipy cvxpy mosek torch matplotlib notebook tqdm seaborn pandas cython. Additional libraries may be needed.

We also recommend to install the MOSEK solver.

How to run the simulations

Example 4.3

All the code to run the simulations in Example 4.3 of the manuscript can be found in the BoundsAnalysis folder. To run the simulations, simply execute the jupyter notebook BoundsAnalysis\run_example.ipynb. Make sure to create a data folder and a figures folder before running the simulations.

Results for the tabular case

Simulations for the tabular case can be found in the RiverSwim folder. To run the simulations, simply execute the file run.py (make sure to adjust the parameters, like NUM_PROCESSES, etc...). Use the make_plots.ipynb to plot the results.

Results for continuous MDPs

1. To run the simulations for the Slipping DeepSea environment, execute the file DeepSea\run_sim.py (make sure to adjust the parameters NUM_PROCESSES, N_SIMS, FREQ_EVAL_GREEDY, etc...).Use the make_plots.ipynb to plot the results.
2. To run the simulations for the Cartpole swingup environment, execute the file CartPoleSwingup\run_sim.py (make sure to adjust the parameters inside the file).Use the make_plots.ipynb to plot the results. Other plots cna be done using the make_entropy_plots.ipynb and make_exploration_plots.ipynb notebooks.

Additional results

• To run the example of the 2-states MDP in the appendix, execute the jupyter notebook in example_2statesMDP\run_example.ipynb.

FAQ/Problems

• If you encounter problems plotting the results using a Jupyter notebook on Linux, remember to install the necessary latex packages sudo apt-get install dvipng texlive-latex-extra texlive-fonts-recommended cm-super (check here [https://stackoverflow.com/questions/11354149/python-unable-to-render-tex-in-matplotlib])
• Sometimes you need to create a data folder to make sure that the results are saved correctly.