How do AI agents learn to play video games effectively?

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AI agents learn to play games using reinforcement learning (RL), where the agent interacts with the game environment and learns through trial and error. In this setup, the agent receives rewards for achieving specific goals (like winning a game or scoring points) and penalties for undesirable actions. Over time, the agent refines its strategies by adjusting its actions to maximize cumulative rewards. Advanced techniques like deep Q-networks (DQN) or Monte Carlo tree search (MCTS) are used for more complex games. These methods allow agents to handle large state spaces and make decisions efficiently, similar to human players, by continuously learning from experience.

Source: https://www.inoru.com/ai-agent-development-company