Reinforcement learning is a type of machine learning that involves training an agent to make a sequence of decisions in an environment in order to maximize a cumulative reward. The agent learns by trial and error, receiving feedback in the form of rewards or punishments for each action it takes.
In game development, reinforcement learning can be used to train game agents to perform various tasks, such as playing a game or controlling a character in a virtual world. The agent interacts with the game environment and receives rewards or punishments based on its actions. By maximizing the rewards it receives over time, the agent can learn to play the game more effectively.
For example, in a game like chess, the agent could learn to play by exploring different move sequences and evaluating the resulting positions. By receiving a positive reward for winning a game and a negative reward for losing, the agent can learn which moves lead to better outcomes and adjust its strategy accordingly.
Reinforcement learning can be challenging to implement in game development due to the complexity of game environments and the need for real-time decision-making. However, it has been successfully applied to a range of games, from classic board games to modern video games.