REINFORCEMENT LEARNING METHOD OF ARTIFICIAL INTELLIGENCE: APPLICATIONS AND CHALLENGES
Keywords:
Reinforcement learning, agent, environment, policy, reward signal, and robotics.Abstract
This paper provides an overview of reinforcement learning (RL) and its potential for various applications, including robotics, game-playing, healthcare, finance, and education. The paper discusses the working principle of RL, including the agent, environment, policy, and reward signal, and explores various RL techniques and algorithms, such as Q-Learning, SARSA, and Deep Reinforcement Learning. The paper also highlights the advantages and limitations of RL and the challenges that must be addressed to unlock its full potentials, such as the difficulty of designing reward functions, the exploration-exploitation trade-off, and the instability of training algorithms. Overall, this paper offers a comprehensive understanding of RL and its potential for solving complex decision-making problems in real-world applications.
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