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Research on Action Selection Techniques and Dynamic Dense Reward Application for Efficient Exploration in Policy-Based Reinforcement Learning
Junhyuk Kim, Junoh Kim, Kyungeun Cho
http://doi.org/10.5626/JOK.2025.52.4.293
Nowadays, reinforcement learning is being studied and utilized in various fields, including autonomous driving, robotics, and gaming. The goal of reinforcement learning is to find the optimal policy for an agent to interact with its environment. Depending on the environment and the specific problem, either a policy-based algorithm or a value-based algorithm is selected for use. Policy-based algorithms can effectively learn in continuous and high-dimensional action spaces, but they face challenges such as the influence of learning rate parameters on the learning process and increased difficulty in converging to an optimized policy in complex environments. To address these issues, this paper proposes a behavior selection technique and a dynamic dense reward design based on a simulated annealing algorithm. The proposed method is applied to two different environments, and experimental results show that the policy-based reinforcement learning algorithms utilizing this method outperform the standard reinforcement learning algorithms.
Reinforcement Learning-based Traffic Signal Control under Real-World Constraints
Mingyu Pi, Hunsoon Lee, Moonyoung Chung
http://doi.org/10.5626/JOK.2021.48.8.871
Traffic signal control plays an important role in efficiently using the limited capacity of the road. Since traditional traffic signal control methods operate based on preset signals, it is difficult to cope with frequently changing traffic conditions. Recently, as reinforcement learning has attracted attention as a method for solving complex problems, studies using reinforcement learning for efficient traffic signal control are being conducted. Compared to the traditional method, it has been proved through simulation that waiting time and travel time were improved. However, since most of the studies did not reflect the limitations of the actual signal, it was designed inappropriately for practical application. In this paper, we proposed a signal control method based on reinforcement learning that could be applied to real situations by reflecting the constraints of the signal operating system that exist in reality, and that could respond to changes in traffic volume.
Deep Reinforcement Learning based Multipath Packet Scheduling
Minwoo Joo, Wonwoo Jang, Wonjun Lee
http://doi.org/10.5626/JOK.2019.46.7.714
Packet scheduling in multipath environments deals with the determination of the manner of distribution of data traffic over multiple network paths and is considered as one of the significant factors affecting the multipath transport performance. However, existing algorithms for packet scheduling rely on particular metrics, which leads to limited performance under dynamic network conditions. In this paper, we propose a deep reinforcement learning (DRL) based packet scheduler with an ability to adapt to dynamic network changes. We have designed a DRL model to automatically capture and discover the network states and effects from the scheduling decisions. The proposed packet scheduler is implemented based on a multipath extension of the Quick UDP Internet Connections (QUIC) network stack and evaluated through network emulation to verify the potential of autonomous networking.
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