Res. Asst. HÜSEYİN AYDIN

Journal articles indexed in SCI, SSCI, and AHCI

Potential-based reward shaping using state–space segmentation for efficiency in reinforcement learning

Bal M. İ., AYDIN H., İYİGÜN C., POLAT F.

Future Generation Computer Systems , vol.157, pp.469-484, 2024 (SCI-Expanded)

Population-based exploration in reinforcement learning through repulsive reward shaping using eligibility traces

Bal M. I., İYİGÜN C., POLAT F., AYDIN H.

Annals of Operations Research , vol.335, no.2, pp.689-725, 2024 (SCI-Expanded)

Using chains of bottleneck transitions to decompose and solve reinforcement learning tasks with hidden states

Aydin H., Çilden E., Polat F.

Future Generation Computer Systems , vol.133, pp.153-168, 2022 (SCI-Expanded)

Papers Presented at Peer-Reviewed Scientific Conferences

Assisting Users in Privacy Conflicts with Partially Observable Multi-Agent Reinforcement Learning

Aydln H.

3rd International Conference on Hybrid Human-Artificial Intelligence, HHAI 2024, Hybrid, Malmo, Sweden, 10 - 14 June 2024, vol.386, pp.55-63

Compact Frequency Memory for Reinforcement Learning with Hidden States.

Polat F., Aydin H., Cilden E.

PRIMA 2019: Principles and Practice of Multi-Agent Systems - 22nd International Conference, Taranto, Italy, 28 - 31 October 2019, pp.425-433

Using Transitional Bottlenecks to Improve Learning in Nearest Sequence Memory Algorithm

AYDIN H., Cilden E., POLAT F.

IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), Massachusetts, United States Of America, 6 - 08 November 2017, pp.147-152

Self-management of patients with severe arthritis through a Personal Health System: The Turkish case study in the PALANTE project

Aydın H., Gençtürk M., Alpay E., L. Ertürkmen G. B., Doğaç A.

eChallenges Conference, Belfast, United Kingdom, 29 - 30 October 2014, pp.1-7

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Res. Asst. HÜSEYİN AYDIN

Publications & Works

Potential-based reward shaping using state–space segmentation for efficiency in reinforcement learning

Population-based exploration in reinforcement learning through repulsive reward shaping using eligibility traces

Using chains of bottleneck transitions to decompose and solve reinforcement learning tasks with hidden states

Assisting Users in Privacy Conflicts with Partially Observable Multi-Agent Reinforcement Learning

Compact Frequency Memory for Reinforcement Learning with Hidden States.

Using Transitional Bottlenecks to Improve Learning in Nearest Sequence Memory Algorithm

Self-management of patients with severe arthritis through a Personal Health System: The Turkish case study in the PALANTE project

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Open Access