This repo contains the code that releases a new family of Deep Reinforcement Learning (DRL) algorithms. The aim of these algorithms is to learn an approximation of the state-value V(s) function alongside an approximation of the state-action value Q(s,a) function. Both approximations learn from each-others estimates, therefore yielding faster and more robust training. This work is an in-depth extension of our original DQV-Learning paper and will be presented in December at the coming NeurIPS Deep Reinforcement Learning (DRLW) Workshop in Vancouver (Canada).

An in depth presentation of the several benefits that these algorithms provide are discussed in our new paper: 'Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning Algorithms'.

Be sure to check out Arxiv for a pre-print of our work!

The main algorithms presented in this repo are:

• Dueling Deep Quality-Value (Dueling-DQV) Learning: This Repo
• Deep Quality-Value-Max (DQV-Max) Learning: This Repo
• Deep Quality-Value (DQV) Learning: originally presented in 'DQV-Learning'', is now properly refactored.

while we also release implementations of:

• Deep Q-Learning: DQN
• Double Deep Q-Learning: DDQN

which have been used for some of the comparisons presented in our work.

If you aim to train an agent from scratch on a game of the Atari Arcade Learning benchmark (ALE) run the training_job.sh script: it allows you to choose which type of agent to train according to the type of policy learning it uses (online for DQV and Dueling-DQV, while offline for all other algorithms). Note that based on which game you choose, some modifications to the code might be required.

In ./models we release a trained model obtained on Pong both for DQV and for DQV-Max.

You can use these models to explore the behavior of the learned value functions with the ./src/test_value_functions.py script. The script will compute the averaged expected return of all visited states and show that the algorithms of the DQV-family suffer less from the overestimation bias of the Q function. The script will also show that our algorithms do not overestimate the V function instead of the Q function.