The application scenarios of recommendation systems are becoming increasingly complex, and traditional algorithms struggle to balance users' short-term click behaviors with their long-term value demands. Reinforcement learning, with its dynamic policy iteration capability for the interaction between the subject and the environment, provides a core technical path for optimizing the long-term value of recommendation systems. This paper focuses on three typical recommendation scenarios: e-commerce, games, and knowledge retrieval. It systematically reviews the algorithm paradigms of reinforcement learning and their applicability in different scenarios, compares the application logic differences between product recommendation and content recommendation, and summarizes the verification results of basic reinforcement learning recommendation models based on public datasets. The research shows that value-based reinforcement learning algorithms are suitable for product recommendation scenarios, while policy-based algorithms are more suitable for content recommendation scenarios. The related research results provide theoretical references and practical basis for the application of reinforcement learning in industrial-level recommendation scenarios.

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Wireless charging, as a new charging method, shows its unique advantages such as no need to plug or unplug connectors, safety and convenience to the world. However, the technology still has some defects in its working efficiency and stability. Some researchers cast their eyes on solar power generation which is more environmentally friendly. They try to combine wireless charging with solar power generation in order to compensate for the shortcomings of both technologies. Based on the theory of wireless charging and solar power generation, this article is going to discuss the feasibility of solar-powered wireless charging technology and analyze reasonable optimization methods for this technology. Through examples, this article draws the following conclusions. First, the efficiency of solar-powered wireless charging technology can be improved by adjusting the circuit structure and coordinating with appropriate algorithms. Then, the material of the coil and the type of power supply can be flexibly matched according to the needs, supplemented by necessary compensation parts, which can greatly reduce the cost of the system. Last, adding functional components is able to enlarge usage scenarios of the technology. Furthermore, homes are great application scenarios for solar-powered wireless charging technology.

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With the advancement of technology, the number of vehicles has increased, and the traffic problems have become more severe. During the morning rush hour, traffic congestion has become a common occurrence. The traditional traffic lights cannot determine when to change the lights as the cycle changes. Therefore, the traditional traffic lights that change according to a fixed cycle are no longer suitable for the current traffic conditions. This experiment aims to investigate the feasibility of an intelligent traffic light control system based on deep reinforcement learning (DQN). By constructing a simplified simulation environment of a crossroads, the DQN intelligent agent was designed to dynamically adjust the phase switching, and a comparative experiment was conducted with the fixed-duration strategy. The aim of this study is to cut down queuing time, lower time costs and fuel consumption. The experimental results show that the DQN agent can autonomously learn the optimal control strategy, reducing the average queue length by approximately 28.6%, verifying the application potential of reinforcement learning in intelligent traffic control. This research conducted a systematic analysis of aspects such as technical feasibility, data feasibility, and one's own capabilities, providing a framework for the development of intelligent traffic lights.

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With the widespread application of large language models in automatic evaluation tasks, Large Language Model (LLM)-as-a-Judge has gradually become an important paradigm for reward modeling and model alignment. Existing research mainly improves judgment ability through thought chain reasoning and reinforcement learning, but pays less attention to the impact of training data distribution structure characteristics on model learning behavior. Addressing the positional bias problem in large language models' generated answers, this paper constructs a controllable synthetic preference data environment from the perspective of data distribution and compares the learning differences of reward models under biased and unbiased training mechanisms. The experiment uses a logistic regression model as the reward function approximator and analyzes the model's accuracy, positional bias rate, and parameter structure changes while keeping the test set unbiased. The results show that when there is a statistical correlation between order and label in the training data, the positional feature weight is 1.047, significantly higher than the control group's weight of 0.303. This indicates that the model significantly increases its dependence on positional features, and even if the overall performance does not decrease significantly (94.67% & 98.33%), its internal decision-making mechanism still shifts. The study reveals that data structure features may alter the model's discrimination criteria without affecting surface performance, providing a mechanism-level reference for reward model design and data construction.

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