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tandem_dqn

README.md

Tandem DQN

This repository provides an implementation of the Tandem DQN agent and main experiments presented in the paper "The Difficulty of Passive Learning in Deep Reinforcement Learning" (Georg Ostrovski, Pablo Samuel Castro and Will Dabney, 2021).

The code is a modified fork of the DoubleDQN agent in the DQN Zoo agent collection.

Quick start

This code can be run with a regular CPU setup, but will only be reasonably fast (with the given configuration) if run with a GPU accelerator, in which case you will need an NVIDIA GPU with recent CUDA drivers. For installation, run

git clone [email protected]:deepmind/deepmind-research.git
virtualenv --python=python3.6 "tandem"
source tandem/bin/activate
cd deepmind_research
pip install -r tandem_dqn/requirements.txt

The code has only been tested with Python 3.6.14

Running the experiments presented in the paper

To execute the vanilla Tandem DQN experiment on the Pong environment, run:

python -m "tandem_dqn.run_tandem" --environment_name=pong --seed=42

A number of flags can be specified to customize execution and run various of the presented experimental variations:

  • --use_sticky_actions (values: True, False; default: False): Use "sticky actions" variant (Machado et al 2017) of the Atari environment.

  • --network_active, --network_passive (values: double_q, qr; default: double_q): Whether to use the DoubleDQN or QR-DQN (Dabney et al 2017) network architecture for active and passive agents (can be set independently). Note this value needs to be compatible with the chosen loss (see below).

  • --loss_active, --loss_passive (values: double_q, double_q_v, double_q_p, double_q_pv, qr, q_regression; default: double_q) Which loss to use for active and passive agent training. The losses are:

    • double_q: regular Double-Q-Learning loss.
    • double_q_v: Double-Q-Learning with bootstrap target values provided by the respective other agent's target network.
    • double_q_p: Double-Q-Learning with bootstrap target policy (argmax) provided by the respective other agent's online network.
    • double_q_pv: Double-Q-Learning with both boostrap target values and policy provided by the respective other agent's target and online networks.
    • qr: Quantile Regression Q-Learning loss (Dabney et al 2017).
    • q_regression: Supervised regression loss towards the respective other agent's online network's output values.

  • --optimizer_active, --optimizer_passive (values: adam, rmsprop; default: rmsprop for both): Which optimization algorithm to use for the active and passive network training (can be set independently).

  • --exploration_epsilon_end_value (values in [0, 1]; default: 0.01): Value of epsilon parameter in active agent's epsilong-greedy policy after an initial decay phase.