Tootfinder

Beyond ReLU: Chebyshev-DQN for Enhanced Deep Q-Networks
Saman Yazdannik, Morteza Tayefi, Shamim Sanisales
https://arxiv.org/abs/2508.14536 https://arxiv.or…

Beyond ReLU: Chebyshev-DQN for Enhanced Deep Q-Networks
The performance of Deep Q-Networks (DQN) is critically dependent on the ability of its underlying neural network to accurately approximate the action-value function. Standard function approximators, such as multi-layer perceptrons, may struggle to efficiently represent the complex value landscapes inherent in many reinforcement learning problems. This paper introduces a novel architecture, the Chebyshev-DQN (Ch-DQN), which integrates a Chebyshev polynomial basis into the DQN framework to create…

Analysis of Bluffing by DQN and CFR in Leduc Hold'em Poker
Tarik Zaciragic, Aske Plaat, K. Joost Batenburg
https://arxiv.org/abs/2509.04125 https://arx…

Analysis of Bluffing by DQN and CFR in Leduc Hold'em Poker
In the game of poker, being unpredictable, or bluffing, is an essential skill. When humans play poker, they bluff. However, most works on computer-poker focus on performance metrics such as win rates, while bluffing is overlooked. In this paper we study whether two popular algorithms, DQN (based on reinforcement learning) and CFR (based on game theory), exhibit bluffing behavior in Leduc Hold'em, a simplified version of poker. We designed an experiment where we let the DQN and CFR agent play ag…

Learning to Assemble the Soma Cube with Legal-Action Masked DQN and Safe ZYZ Regrasp on a Doosan M0609
Jaehong Oh, Seungjun Jung, Sawoong Kim
https://arxiv.org/abs/2508.21272 ht…

Learning to Assemble the Soma Cube with Legal-Action Masked DQN and Safe ZYZ Regrasp on a Doosan M0609
This paper presents the first comprehensive application of legal-action masked Deep Q-Networks with safe ZYZ regrasp strategies to an underactuated gripper-equipped 6-DOF collaborative robot for autonomous Soma cube assembly learning. Our approach represents the first systematic integration of constraint-aware reinforcement learning with singularity-safe motion planning on a Doosan M0609 collaborative robot. We address critical challenges in robotic manipulation: combinatorial action space expl…

An Arbitration Control for an Ensemble of Diversified DQN variants in Continual Reinforcement Learning
Wonseo Jang, Dongjae Kim
https://arxiv.org/abs/2509.04815 https://

An Arbitration Control for an Ensemble of Diversified DQN variants in Continual Reinforcement Learning
Deep reinforcement learning (RL) models, despite their efficiency in learning an optimal policy in static environments, easily loses previously learned knowledge (i.e., catastrophic forgetting). It leads RL models to poor performance in continual reinforcement learning (CRL) scenarios. To address this, we present an arbitration control mechanism over an ensemble of RL agents. It is motivated by and closely aligned with how humans make decisions in a CRL context using an arbitration control of m…

Breaking the Cold-Start Barrier: Reinforcement Learning with Double and Dueling DQNs
Minda Zhao
https://arxiv.org/abs/2508.21259 https://arxiv.org/pdf/2508…

Breaking the Cold-Start Barrier: Reinforcement Learning with Double and Dueling DQNs
Recommender systems struggle to provide accurate suggestions to new users with limited interaction history, a challenge known as the cold-user problem. This paper proposes a reinforcement learning approach using Double and Dueling Deep Q-Networks (DQN) to dynamically learn user preferences from sparse feedback, enhancing recommendation accuracy without relying on sensitive demographic data. By integrating these advanced DQN variants with a matrix factorization model, we achieve superior perform…

HCQA: Hybrid Classical-Quantum Agent for Generating Optimal Quantum Sensor Circuits
Ahmad Alomari, Sathish A. P. Kumar
https://arxiv.org/abs/2508.21246 https://

HCQA: Hybrid Classical-Quantum Agent for Generating Optimal Quantum Sensor Circuits
This study proposes an HCQA for designing optimal Quantum Sensor Circuits (QSCs) to address complex quantum physics problems. The HCQA integrates computational intelligence techniques by leveraging a Deep Q-Network (DQN) for learning and policy optimization, enhanced by a quantum-based action selection mechanism based on the Q-values. A quantum circuit encodes the agent current state using Ry gates, and then creates a superposition of possible actions. Measurement of the circuit results in prob…

A Deep Q-Network based power control mechanism to Minimize RLF driven Handover Failure in 5G Network
Kotha Kartheek, Shankar K. Ghosh, Megha Iyengar, Vinod Sharma, Souvik Deb
https://arxiv.org/abs/2510.05762

A Deep Q-Network based power control mechanism to Minimize RLF driven Handover Failure in 5G Network
The impact of Radio link failure (RLF) has been largely ignored in designing handover algorithms, although RLF is a major contributor towards causing handover failure (HF). RLF can cause HF if it is detected during an ongoing handover. The objective of this work is to propose an efficient power control mechanism based on Deep Q-Network (DQN), considering handover parameters (i.e., time-to-preparation, time-to-execute, preparation offset, execution offset) and radio link monitoring parameters (T…

Adaptive Vehicle Speed Classification via BMCNN with Reinforcement Learning-Enhanced Acoustic Processing
Yuli Zhang, Pengfei Fan, Ruiyuan Jiang, Hankang Gu, Dongyao Jia, Xinheng Wang
https://arxiv.org/abs/2509.00839

Adaptive Vehicle Speed Classification via BMCNN with Reinforcement Learning-Enhanced Acoustic Processing
Traffic congestion remains a pressing urban challenge, requiring intelligent transportation systems for real-time management. We present a hybrid framework that combines deep learning and reinforcement learning for acoustic vehicle speed classification. A dual-branch BMCNN processes MFCC and wavelet features to capture complementary frequency patterns. An attention-enhanced DQN adaptively selects the minimal number of audio frames and triggers early decisions once confidence thresholds are reac…

Learning Obstacle Avoidance using Double DQN for Quadcopter Navigation
Nishant Doshi, Amey Sutvani, Sanket Gujar
https://arxiv.org/abs/2509.18734 https://a…

Crosslisted article(s) found for stat.CO. https://arxiv.org/list/stat.CO/new
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- Learning to Assemble the Soma Cube with Legal-Action Masked DQN and Safe ZYZ Regrasp on a Doosan ...
Jaehong Oh, Seungjun Jung, Sawoong Kim