aftab 0.1.30

A highly configurable implementation of our approach in the Aftab paper, benchmarking different convolutional neural networks and their effects on the final results.

Overview

Aftab (Persian: آفتاب, meaning "sun" or "sun rays") is a benchmarking framework for evaluating CNN-based encoders in PQN across Atari environments.
It provides standardized training, evaluation, and reproducibility tools for deep reinforcement learning research.

IQM HNS	IQM HNS (Last 50M Frames)

Global performance of base encoders.

IQM HNS	IQM HNS (Last 50M Frames)

Comparison of two Gamma encoder variants based on findings from Hadamax Encoding: Elevating Performance in Model-Free Atari .

Installation

Install via pip:

pip install aftab

Usage

from aftab import Aftab
from aftab import aftab_environments

seeds = [1, 2, 3, 4]

for environment in aftab_environments:
    agent = Aftab(encoder="gamma", frames="pilot")
    for seed in seeds:
        agent.train(environment=environment, seed=seed)
        agent.log()

Defining a Custom Encoder

You can define your own encoder as a PyTorch module and pass it to the agent:

import torch
from aftab import Aftab

class CustomImageEncoder(torch.nn.Module):
    def __init__(self):
        super().__init__()
  
    def forward(self, x):
        pass

agent = Aftab(encoder=CustomImageEncoder, frames="pilot")

Results

Base Encoder Experiments

• HNS
• Scores

Hadamax Experiments

• HNS
• Scores

Note: The Eta variant has significantly more parameters than other variants, primarily due to the encoder producing a large number of features.

Parameter Count

Base Encoder Variations

Variant	Encoder Parameters	Q Regression Head	Total Parameters
PQN	78,304	1,686,500	1,764,804
Alpha	174,752	1,782,948	1,957,700
Beta	89,008	1,782,948	1,871,956
Gamma	117,168	1,725,364	1,842,532
Delta	78,552	1,850,588	1,929,140
Epsilon	80,112	2,179,828	2,259,940
Zeta	77,232	2,537,396	2,614,628
Eta	78,400	23,739,460	23,817,860
Theta	76,288	1,127,428	1,203,716

Hadamax Variants

Variant	Encoder Parameters	Q Regression Head	Total Parameters
PQN Hadamax	156,608	3,968,516	4,125,124
Gamma Hadamax V1	234,336	1,609,220	1,843,556
Gamma Hadamax V2	234,336	3,280,388	3,514,724

Hyperparameters

Hyperparameter	Value
Learning rate	$2.5 \times 10^{-4}$
Training environments	128
Test environments	8
Optimizer	Rectified Adam
Weight decay	0
$\epsilon$	$1 \times 10^{-5}$
$\beta_{1}$	0.9
$\beta_{2}$	0.999
Total Frames	200,000,000
Loss function	Mean Squared Error
Scheduler	Linear Annealing
$\epsilon$-greedy exploration	10% of total frames
Discount factor ($\gamma$)	0.99
GAE ($\lambda$)	0.65
Epochs	2
Batch size	4096

Used in encoder and Hadamax experiments.

Statistical Significance

	PQN	Alpha	Beta	Gamma	Delta	Epsilon	Zeta	Eta	Theta
PQN	-	-	-	-	-	-	-	-	-
Alpha	0	-	-	-	-	-	-	-	-
Beta	0	0.847	-	-	-	-	-	-	-
Gamma	0	0.295	0.802	-	-	-	-	-	-
Delta	0	0	0	0	-	-	-	-	-
Epsilon	0	0.104	0.068	0.01	0	-	-	-	-
Zeta	0	0.145	0.293	0.024	0	0.552	-	-	-
Eta	0.001	0.337	0.757	0.221	0	0.819	0.967	-	-
Theta	0.431	0	0.004	0	0.046	0.001	0.001	0.002	-

	Gamma	Hadamax Gamma V1	Hadamax Gamma V2	Hadamax
Gamma	-	-	-	-
Hadamax Gamma V1	0	-	-	-
Hadamax Gamma V2	0	0.72	-	-
Hadamax Nature DQN	0	0.078	0.151	-

Reproducibility

Due to the stochastic nature of deep reinforcement learning, exact reproducibility via fixed datasets is not feasible.
Instead, we provide a set of random seeds used in our experiments.

from aftab import aftab_seeds

print(aftab_seeds)

Full experiment replication:

from aftab import Aftab
from aftab import aftab_environments
from aftab import aftab_seeds

for environment in aftab_environments:
    agent = Aftab()
    for seed in aftab_seeds:
        agent.train(environment=environment, seed=seed)
        agent.log()

A comprehensive set of Atari environments is available via EnvPool:
https://envpool.readthedocs.io/en/latest/env/atari.html#available-tasks

Citation

@article{aftab2026benchmarking,
  title={Aftab: Benchmarking {CNN} Encoders in {PQN}},
  author={Shieenavaz, Taha and Zareshahraki, Shabnam and Nanni, Loris},
  journal={arXiv preprint arXiv:YYMM.NNNNN},
  year={2026}
}

Related Works

@misc{2107.09645,
  Author = {Denis Yarats and Rob Fergus and Alessandro Lazaric and Lerrel Pinto},
  Title = {Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning},
  Year = {2021},
  Eprint = {arXiv:2107.09645},
}

@misc{2403.03950,
  Author = {Jesse Farebrother and Jordi Orbay and Quan Vuong and Adrien Ali Taïga and Yevgen Chebotar and Ted Xiao and Alex Irpan and Sergey Levine and Pablo Samuel Castro and Aleksandra Faust and Aviral Kumar and Rishabh Agarwal},
  Title = {Stop Regressing: Training Value Functions via Classification for Scalable Deep RL},
  Year = {2024},
  Eprint = {arXiv:2403.03950},
}

License

© 2025 Taha Shieenavaz.
Licensed under CC BY-NC 4.0: https://creativecommons.org/licenses/by-nc/4.0/