Do you know what is Montezuma’s Revenge?
It is a game and also a type of infection!
We will stick to the former for now!
The relevance of this game is in the development of the AI!
A Brief History of Intelligence by Max S Bennet traces the development of intelligence and how it applies to the development of the AI!
Since it is a novel written by a non medical person with a tech background, for a medico it is a very refreshing new outlook on the development of intelligence!
Now how it develops into the Artificial Intelligence is the key point here!
Now Montezuma’s Revenge is a classic platform-adventure video game originally released in 1984.
Designed by Robert Jaeger and published by Parker Brothers, it is often cited as an early precursor to the “Metroidvania” genre due to its open-exploration and backtracking mechanics.
The objective of the game is to Navigate multiple floors to reach a central Treasure Chamber, collecting jewels while avoiding hazards!
The issue here is that for humans this is a routine game but for AI with no scope of a right or a wrong way, the game is difficult to say the least!
The game in fact serves as a primary benchmark for testing “hard-exploration” in Artificial Intelligence, particularly in reinforcement learning (RL).
Because the game requires a long sequence of non-obvious actions to gain any points (sparse rewards), it exposed critical failures in early AI models, leading to significant advancements in how AI explores and learns!
Most AI agents (like DQN) failed completely on this game because they rely on immediate feedback (points) to learn! Which means that there is a right way and wrong in simple games! But in this game the reward is in the end so there is no wrong or right way!
Montezuma’s Revenge is a puzzle-platformer that requires navigating complex, trap-filled, multi-room, and multi-level, requiring agents to climb, jump, and collect items (keys/swords) in a specific order before receiving a single reward!
Early attempts to fix this involved giving AI “curiosity” (intrinsic motivation) to explore new areas. However, agents became obsessed with unpredictable, high-entropy, or “noisy” areas (like flickering screens) rather than useful gameplay, a failure known as the “noisy TV” problem!
Then key AI advancements were developed specifically to conquer Montezuma’s Revenge!
One algorithm called the Go-Explore (Uber AI) successfully solved the game by prioritizing “exploring from promising states” rather than relying on random exploration. It memorized “visited” game states, allowing it to revisit them and continue exploring from there, rather than starting over!
Finally an AI system called Agent57 or the DeepMind was the first to achieve superhuman performance across all 57 Atari games, including Montezuma’s Revenge, by combining advanced exploration strategies with a meta-controller to balance short- and long-term behavior!
The key techniques used was called the Reinforcement Learning (RL) where the agent plays by trial-and-error, receiving rewards or penalties!
Mastering Montezuma’s Revenge is considered a milestone because the techniques developed—specifically, intelligent, curiosity-driven, and memory-based exploration—are directly applicable to real-world AI challenges, such as robotic navigation in unknown, complex environments (e.g., search and rescue, home automation etc.!)
So how does an AI demonstrate intelligence? By defeating a Video game released more than 30 years back!
Then again Kavita Krishnamurthy who has been active for more than 40 years now!
That is real natural talent!
Now stop playing Montezuma’s Revenge and sleep!
SHubh Ratri!
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