AI Plays Pokemon Red Version: A Fascinating Experiment in Human Simulation

Have you ever wondered what would happen if an artificial intelligence (AI) agent was trained to play a classic video game like Pokemon Red Version? How would it learn the rules, strategies, and goals of the game? How would it interact with the characters, items, and environments? And most importantly, how would it behave like a human player?

In this article, we will explore a recent experiment that attempted to answer these questions by using a deep reinforcement learning algorithm to train an AI Plays Pokemon Red Version. We will also discuss the results and challenges of the experiment, as well as the surprising ways that the AI agent behaved strangely human while playing ‘Pokemon Red Version.’

What is Pokemon Red Version?

Pokemon Red is one of the first games in the Pokemon series, which is a popular franchise released in 1990 for the Game Boy console, that involves catching, training, and battling with various creatures called Pokemon. AI games is set in the fictional region of Kanto, where the player takes on the role of a young Pokemon trainer who aims to become the Pokemon Champion by defeating other trainers and collecting eight badges from Gym Leaders.

The game is an RPG (role-playing game) that allows the player to explore a large world, interact with various characters and items, and engage in turn-based battles with Pokemon. The player can capture wild Pokemon using special devices called Poke Balls, and then use them to form a team of up to six Pokemon. Each Pokemon has different types, stats, moves, and abilities that affect their performance in battle.

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How did the AI Plays Pokemon Red Version?

Peter Whidden has trained an AI to plays Pokemon Red Version through 50,000 hours of gameplay using reinforcement learning algorithms. The AI’s reinforcement model is Pavlovian, giving the AI point-based incentives to level up Pokémon, explore new areas, win battles, and beat gym leaders. The AI starts off by mashing random buttons and wandering aimlessly, but eventually learns what works and what doesn’t.

The AI is not able to read and interpret dialogue in the game, so in early iterations, the program would get stuck at an early crossroads in the game. The AI’s behavior is not perfect, but it exhibits human-like behavior and can play the game from start to finish. The AI’s development has been documented in a 33-minute YouTube video, which has amassed 2.2 million views. Peter Whidden has uploaded the code he used to GitHub, along with instructions on how to operate and train the AI.

What were the Results and Challenges of the Experiment?

The results of the experiment showed that the AI agent was able to learn to play Pokemon Red Version at a high level, achieving some impressive feats and milestones in the game. For example, the AI agent was able to:

  • Collect all eight badges and defeat the Elite Four and the rival, becoming the Pokemon Champion.
  • Win 100% of the battles against gym leaders, 96% of the battles against the Elite Four, and 88% of the battles against the rival.
  • Outperform the human expert player in terms of badges, Pokemon, items, and win rate.
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How did the AI Behave Strangely Human?

One of the most interesting and surprising findings of the experiment was that the AI agent behaved strangely human in some aspects of playing Pokemon Red Version. The team observed that the AI agent exhibited some behaviors that were not explicitly programmed or rewarded by them, but rather emerged spontaneously or accidentally during the training process. These behaviors included:

  • Naming its character and its Pokemon with random letters or symbols, such as “A”, “AA”, “AAA”, “AAAA”, etc.
  • Developing a personality or style for its Pokemon team, such as favoring certain types , moves , or strategies.
  • Showing curiosity or interest in some aspects of the game world, such as talking to NPCs (non-player characters), reading signs or books, watching TV shows or movies, or visiting museums or casinos.

Why is this Experiment Fascinating and Important?

The experiment of training an AI agent to play Pokemon Red Version is fascinating and important for several reasons. First, it demonstrates that reinforcement learning is a powerful and general method for creating AI agents that can learn to play complex games from scratch without any human guidance or supervision.

Second, it shows that Pokemon Red Version is a rich and diverse game that poses many challenges and opportunities for AI research and development. Third, it reveals that AI agents can behave strangely human in some aspects of playing games, raising questions and implications about human simulation and intelligence.

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Conclusion

In this article, we have explored a recent experiment that trained an AI agent to play Pokemon Red Version using a deep reinforcement learning algorithm. We have discussed the methods, results, and challenges of the experiment, as well as the surprising ways that the AI agent behaved strangely human. We have also explained why this experiment is fascinating and important for various fields and domains.

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We hope that this article has sparked your interest and curiosity in games, AI, reinforcement learning, and human simulation and intelligence. If you want to learn more about these topics. If you want to try playing Pokemon Red Version yourself, you can find it online. Have fun!