Hello everyone, I am Wei Xue AI. Today I will introduce to you the application of deep learning in practice 62-reinforcement learning in the field of simple games, using reinforcement learning to train the code and steps of the Agent program. This article shows how to use reinforcement learning to build agent programs without using heuristics. By playing the game and trying to maximize the winning rate, we can gradually refine the strategy of the Agent program. Reinforcement learning is a machine learning method in which the Agent program learns the optimal strategy by interacting with the environment and adjusts behavior through reward signals. This article will introduce in detail the code and steps for training Agent programs using reinforcement learning.
introduction
Reinforcement learning is a machine learning method used to train intelligent agent programs to gradually improve performance during interaction with the environment. Unlike supervised learning, reinforcement learning does not require labeled training data, but instead obtains feedback and reward signals through interaction with the environment. The Agent program continuously interacts with the environment, selects actions based on the current state, and updates strategies based on feedback from the environment. In this way, the Agent program can gradually learn the optimal strategy in a given environment.
In this article, we will introduce the code and steps for training an Agent program using reinforcement learning. First, we need to choose a game as the environment for the Agent program. This can be a simple board game like Tic-Tac-Toe or a more complex video game like an Atari game. Next, we need to define the state space, action space and reward function of the Agent program. The state space of the Agent program is a set of variables that describe the current state of the environment. In the tic-tac-toe game, the state space can be a 3x3 chessboard, representing the status of the chess pieces in each position. The action space of the Agent program is the set of actions that the Agent program can choose. In the game of Tic-Tac-Toe, the action space can be all playable positions. The reward function is used to evaluate the behavior of the Agent program and provide a feedback signal. In the game of Tic-Tac-Toe, winning actions can be rewarded positively, while failed actions can be rewarded negatively.
We can then use reinforcement learning algorithms such as Q-learning or deep reinforcement learning algorithms such as DQN,