aftab 0.1.56

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

Note that the JAX API is under development, but using current PyTorch version you need to expect training of your agents to take up to 13 hours for the best configuration. We hope we are going to get much faster results using JAX.

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

Encoder Experiments:

• Tables:
  • HNS
  • Scores
• Charts:
  • Loss Evolution
  • IQM HNS

Hadamax Experiments:

• Tables:
  • HNS
  • Scores
• Charts:
  • Loss Evolution
  • IQM HNS

Final Experiments: (GPUs are working :D)

Model Complexity

Base Variants

Variant	Encoder Parameters	Regression Head Parameters	Total Parameters	Encoder FLOPs	Regression Head FLOPs	Total FLOPs
PQN	78,304	1,686,500	1,764,804	7.734	1.610	9.347
Alpha	174,752	1,782,948	1,957,700	27.541	1.610	29.151
Beta	89,008	1,782,948	1,871,956	61.515	1.610	63.126
Gamma	117,168	1,725,364	1,842,532	22.901	1.610	24.512
Delta	78,552	1,850,588	1,929,140	6.143	1.774	7.917
Epsilon	80,112	2,179,828	2,259,940	13.252	2.101	15.354
Zeta	77,232	2,537,396	2,614,628	25.362	2.462	27.824
Eta	78,400	23,739,460	23,817,860	28.422	23.663	52.085
Theta	76,288	1,127,428	1,203,716	9.065	1.053	10.118

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

---

Hadamax Variants

Variant	Encoder Parameters	Regression Head Parameters	Total Parameters	Encoder FLOPs	Regression Head FLOPs	Total FLOPs
PQN Hadamax	156,608	3,968,516	4,125,124	159.014	3.969	162.984
Gamma Hadamax V1	234,336	1,609,220	1,843,556	122.001	1.610	123.611
Gamma Hadamax V2	234,336	3,280,388	3,514,724	129.300	3.281	132.581

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

Hardware

Nvidia A40 GPUs were used to run all the experiments in this experiment.

Specification	Details
GPU Memory	48 GB GDDR6 with error-correcting code (ECC)
GPU Memory Bandwidth	696 GB/s
Interconnect	NVIDIA NVLink 112.5 GB/s (bidirectional); PCIe Gen4: 64 GB/s
NVLink	2-way low profile (2-slot)
Display Ports	3x DisplayPort 1.4*
Max Power Consumption	300 W
Form Factor	4.4" (H) x 10.5" (L), Dual Slot
Thermal	Passive
vGPU Software Support	NVIDIA Virtual PC, NVIDIA Virtual Applications, NVIDIA RTX Virtual Workstation, NVIDIA Virtual Compute Server, NVIDIA AI Enterprise
vGPU Profiles Supported	See the Virtual GPU Licensing Guide
NVENC / NVDEC	1x / 2x (includes AV1 decode)
Secure Boot	Secure and Measured Boot with Hardware Root of Trust (optional)
NEBS Ready	Level 3
Power Connector	8-pin CPU

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

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  Title = {Simplifying Deep Temporal Difference Learning},
  Author = {Matteo Gallici and Mattie Fellows and Benjamin Ellis and Bartomeu Pou and Ivan Masmitja and Jakob Nicolaus Foerster and Mario Martin},
  Year = {2024},
  Eprint = {arXiv:2407.04811},
}

@misc{2403.03950,
  Title = {Stop Regressing: Training Value Functions via Classification for Scalable Deep RL},
  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},
  Year = {2024},
  Eprint = {arXiv:2403.03950},
}

@misc{1511.06581,
  Title = {Dueling Network Architectures for Deep Reinforcement Learning},
  Author = {Ziyu Wang and Tom Schaul and Matteo Hessel and Hado van Hasselt and Marc Lanctot and Nando de Freitas},
  Year = {2015},
  Eprint = {arXiv:1511.06581},
}

@misc{1806.04613,
  Title = {Improving Regression Performance with Distributional Losses},
  Author = {Ehsan Imani and Martha White},
  Year = {2018},
  Eprint = {arXiv:1806.04613},
}

@misc{1602.04621,
  Title = {Deep Exploration via Bootstrapped DQN},
  Author = {Ian Osband and Charles Blundell and Alexander Pritzel and Benjamin Van Roy},
  Year = {2016},
  Eprint = {arXiv:1602.04621},
}

License

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