Game playing

Game playing is a classic problem in RL, where the agent interacts with a game environment and learns to take actions that maximize its score or win rate. The agent learns by trial and error, exploring different strategies and learning from the consequences of its actions. In-game playing with RL, the agent typically receives an observation of the game state as input and outputs an action to take.

The environment then updates the game state and returns a reward signal to the agent, based on the outcome of the action. The agent uses this reward signal to update its policy, which is its strategy for taking action in the future. Over time, the agent learns to make better decisions that maximize its rewards and can achieve superhuman performance in some games. Game playing with RL has been successfully applied to a variety of games, including chess, Go, poker, and video games.