Mechanical Properties of Cement Composites Using Modified Plastics by Gamma Irradiation

Recently, pollution caused by an increasing amount of worldwide plastic waste has become a global problem. However, these concerns can be alleviated by the use of gamma-ray technology. Using radiation technology, plastic wastes can be converted into a variety of useful purposes presenting powerful opportunities for environmental sustainability and material innovations. Plastics are strong, durable, waterproof, lightweight, easy to mold, and recyclable. In this study, plastic aggregate modified by gamma irradiation was mixed into cement composites, and mechanical property evaluation experiments were conducted. As a result, it was confirmed that the physical performance of cement composites was improved by up to 70% in the case of using the modified plastic aggregates compared to the general plastic aggregate

Reinforcement Learning Guided by Double Replay Memory

Experience replay memory in reinforcement learning enables agents to remember and reuse past experiences. Most of the reinforcement models are subject to single experience replay memory to operate agents. In this article, we propose a framework that accommodates doubly used experience replay memory, exploiting both important transitions and new transitions simultaneously. In numerical studies, the deep Q-networks (DQN) equipped with double experience replay memory are examined under various scenarios. A self-driving car requires an automated agent to figure out when to adequately change lanes on the real-time basis. To this end, we apply our proposed agent to the simulation of urban mobility (SUMO) experiments. Besides, we also verify its applicability to reinforcement learning whose action space is discrete (e.g., computer game environments). Taken all together, we conclude that the proposed framework outperforms priorly known reinforcement learning models in the virtue of double experience replay memory