README.md

UCB-H+

This repository contains the source code and data for the experiments presented in Generalized Optimistic Q-Learning with Provable Efficiency.

How to use

Run python3 main.py Replacement-v0 -v and python3 main.py Lake-v0 -v to get the results from the paper. To see the full list of parameters, run python3 main.py -h. If any parameters are missing, the defaults from the defaults file are used instead.

By default, no information is printed to console, and the results are saved to ./results. You can change this using a verbosity flag -v. Use -v, -vv, etc. to control how much information you want to be printed.

If you want to apply the methods to your custom environment, you can see how the agents are used in run.py.

Contents

• ./agent contains classes for the three agents as well as abstract base classes:
  • q_ucb_h_learning.py for UCB-H,
  • q_ucb_h_plus_learning.py for UCB-H+,
  • simple_q_learning_agent.py for Q-Learning.
• ./environment contains two environments used in the paper; The versions of the environments from the paper are registered in OpenAI Gym to use via gym.make(), see ./environment/__init__.py.
  • frozen_lake is the adjustable FrozenLake environment that allows to change the slipping probability. The registered version is Lake-v0.
  • replacement is the Replacement environment. The registered version is Replacement-v0.
• ./process_results is a collection of helper methods for saving and plotting the data.
• ./results is the default directory to save the experiments data to.

Citation

Please cite the paper if you use it:

@inproceedings{Neustroev2020,
  title     = {Generalized Optimistic {Q-Learning} with Provable Efficiency},
  author    = {Neustroev, Grigory and de~Weerdt, Mathijs M.},
  booktitle = {International Conference on Autonomous Agents and Multi-Agent Systems},
  year      = {2020},
  address   = {Auckland, New Zealand},
  publisher = {IFAAMAS},
  month     = {May},
  pages     = {913--921}
}