title: Developing an adaptive Intelligence agent for game theory based on reinforcement learning
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð°Ð³ÐµÐ½ÑÐ° Ð¸Ð³ÑÐ¾Ð²Ð¾Ð¹ Ð°Ð´Ð°Ð¿ÑÐ°ÑÐ¸Ð¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼
Creators: name: Ð¡Ð°Ð½ÑÐµÑ ÐÐ¾Ð¼ÐµÑ, ÐÐµÐ¸ÑÐ¾Ð½
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐ³Ñ ÑÐµÐ¾ÑÐ¸Ñ
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼
ÐÐ¾Ð½ÑÐµ-ÐÐ°ÑÐ»Ð¾
q-Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð°ÐºÑÐµÑ-ÐºÑÐ¸ÑÐ¸Ðº
reinforcement learning
monte carlo
q-learning
actor-critic

description: Ð ÑÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¸Ð·ÑÑÐ°ÐµÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼ Ð´Ð»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð°Ð³ÐµÐ½ÑÐ° Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÐ° Ð² Ð²Ð¸Ð´ÐµÐ¾Ð¸Ð³ÑÐ°Ñ, ÑÐ°ÐºÐ¸Ñ ÐºÐ°Ðº Ð¸Ð³ÑÑ-Ð»Ð°Ð±Ð¸ÑÐ¸Ð½ÑÑ. Ð Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¸ ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°ÑÑÑÑ Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÑÑÑÑÑ ÑÐµÑÑÑ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ð»ÐµÑÐ¾Ð²: ÐÐ¾Ð½ÑÐµ-ÐÐ°ÑÐ»Ð¾, SARSA, SARSA(Î»), Q-learning, REINFORCE Ð¸ Actor-Critic. ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð²ÐºÐ»ÑÑÐ°ÐµÑ Ð² ÑÐµÐ±Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð°Ð³ÐµÐ½ÑÐ° ÐÐ ÐºÐ°Ð¶Ð´Ð¾Ð¼Ñ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ð»ÐµÑÑ Ð¸ Ð¾ÑÐµÐ½ÐºÑ ÐµÐ³Ð¾ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÑÑ, ÑÑÐ¾ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ð»ÐµÑÑ Ð¸Ð¼ÐµÑÑ ÑÐ°Ð·Ð½ÑÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð² Ð¸Ð³ÑÐ¾Ð²Ð¾Ð¹ ÑÑÐµÐ´Ðµ. ÐÐ¾Ð½ÑÑÐ¾Ð»Ð»ÐµÑ ÐÐ¾Ð½ÑÐµ-ÐÐ°ÑÐ»Ð¾ Ð¿ÑÐµÐ²Ð¾ÑÑÐ¾Ð´ÐµÐ½ Ð² ÑÐ°Ð·Ð²ÐµÐ´ÐºÐµ, Ð½Ð¾ Ð¼Ð¾Ð¶ÐµÑ Ð¼ÐµÐ´Ð»ÐµÐ½Ð½Ð¾ ÑÑÐ¾Ð´Ð¸ÑÑÑÑ. SARSA Ð¸ SARSA(Î») Ð¾Ð±ÐµÑÐ¿ÐµÑÐ¸Ð²Ð°ÑÑ Ð±Ð°Ð»Ð°Ð½Ñ Ð¼ÐµÐ¶Ð´Ñ ÑÐ°Ð·Ð²ÐµÐ´ÐºÐ¾Ð¹ Ð¸ ÑÐºÑÐ¿Ð»ÑÐ°ÑÐ°ÑÐ¸ÐµÐ¹, Ð¿ÑÐ¸ ÑÑÐ¾Ð¼ SARSA(Î») Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÑ ÑÑÐ°ÑÑÑ, Ð¿ÑÐ¸Ð³Ð¾Ð´Ð½ÑÐµ Ð´Ð»Ñ Ð´Ð¾Ð»Ð³Ð¾ÑÑÐ¾ÑÐ½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Qlearning Ð´Ð¾ÑÑÐ¸Ð³Ð°ÐµÑ ÑÐ¾ÑÐ¾ÑÐ¸Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð², Ð¸Ð·ÑÑÐ°Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÑ Ð¿Ð¾Ð»Ð¸ÑÐ¸ÐºÑ Ð¿Ð¾ÑÑÐµÐ´ÑÑÐ²Ð¾Ð¼ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð´ÐµÐ¹ÑÑÐ²Ð¸Ñ. REINFORCE Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑ Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÑ Ð¿Ð¾Ð»Ð¸ÑÐ¸ÐºÐ¸ Ð¸ Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÐµÑ Ð¼Ð½Ð¾Ð³Ð¾Ð¾Ð±ÐµÑÐ°ÑÑÐ¸Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð² Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¿Ð¾Ð»Ð¸ÑÐ¸ÐºÐ¸. ÐÐºÑÐµÑ-ÐºÑÐ¸ÑÐ¸Ðº ÑÐ¾ÑÐµÑÐ°ÐµÑ Ð² ÑÐµÐ±Ðµ Ð¼ÐµÑÐ¾Ð´Ñ, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐµ Ð½Ð° ÑÐµÐ½Ð½Ð¾ÑÑÑÑ Ð¸ Ð¿Ð¾Ð»Ð¸ÑÐ¸ÐºÐµ, ÑÑÐ¾ Ð¿ÑÐ¸Ð²Ð¾Ð´Ð¸Ñ Ðº ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾Ð¼Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð­ÑÐ¸ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¸Ð¼ÐµÑÑ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ðµ Ð´Ð»Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸Ð³Ñ, Ð¿ÑÐ¾ÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð°Ð³ÐµÐ½ÑÐ¾Ð² Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÐ° Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼. ÐÐ´Ð°Ð¿ÑÐ¸Ð²Ð½ÑÐ¹ Ð°Ð³ÐµÐ½Ñ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÐ°, ÑÐ¾Ð·Ð´Ð°Ð½Ð½ÑÐ¹ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÑÐ¸Ñ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ð»ÐµÑÐ¾Ð², Ð¼Ð¾Ð¶ÐµÑ ÑÐ»ÑÑÑÐ¸ÑÑ Ð¸Ð³ÑÐ¾Ð²Ð¾Ð¹ Ð¿ÑÐ¾ÑÐµÑÑ Ð¸ ÑÑÐ¾Ð²ÐµÑÑÐµÐ½ÑÑÐ²Ð¾Ð²Ð°ÑÑ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½ÑÐµ Ð¸Ð³ÑÐ¾Ð²ÑÐµ Ð°Ð³ÐµÐ½ÑÑ. Ð Ð·Ð°ÐºÐ»ÑÑÐµÐ½Ð¸Ðµ, ÑÑÐ¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¿Ð¾Ð´ÑÐµÑÐºÐ¸Ð²Ð°ÐµÑ ÑÐ¸Ð»ÑÐ½ÑÐµ Ð¸ ÑÐ»Ð°Ð±ÑÐµ ÑÑÐ¾ÑÐ¾Ð½Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ð»ÐµÑÐ¾Ð² Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼ Ð² Ð¸Ð³ÑÐµ-Ð»Ð°Ð±Ð¸ÑÐ¸Ð½ÑÐµ. ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð´Ð°ÐµÑ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ Ð¾ Ð²ÑÐ±Ð¾ÑÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¸Ñ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸Ðº Ð¸ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸, ÑÐ¿Ð¾ÑÐ¾Ð±ÑÑÐ²ÑÑ Ð»ÑÑÑÐµÐ¼Ñ Ð¿Ð¾Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼ Ð¸ ÐµÐ³Ð¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¿ÑÐ¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½ÑÑ Ð°Ð³ÐµÐ½ÑÐ¾Ð² ÐÐ.
description: This research investigates the use of reinforcement learning algorithms to create an adaptive AI agent in video games such as maze games. The study compares and analyzes six controllers: Monte Carlo, SARSA, SARSA(Î»), Q-learning, REINFORCE, and Actor-Critic. The research involves training the AI agent with each controller and evaluating their performance. The results demonstrate that the controllers have varying effectiveness in a game environment. The Monte Carlo controller excels in exploration but may converge slowly. SARSA and SARSA(Î») strike a balance between exploration and exploitation, with SARSA(Î») offering eligibility traces for long-term learning. Qlearning achieves good results by learning an optimal policy through action-value estimation. REINFORCE utilizes policy gradients and shows promising performance in policy optimization. Actor-Critic combines value-based and policybased methods, leading to efficient learning. These findings have implications in game development, AI agent design, and reinforcement learning algorithms. The adaptive AI agent created with these controllers can improve gameplay experiences and advance intelligent gaming agents. In conclusion, this research highlights the strengths and weaknesses of different reinforcement learning controllers in a maze game. The research provides insights for selecting appropriate algorithms based on their characteristics and performance, contributing to a better understanding of reinforcement learning and its application in developing adaptive AI agents.