Reinforcement learning is a type of machine learning which focuses on methods that enable agents to learn to maximize some posited conception of cumulative reward. It has been become a core method of AI and machine learning research and practice. Atari games have been a long-standing benchmark in the reinforcement learning (RL) community for the past decade.
At the time of writing this question, the model Go-Explore (Ecoffet et al., 2020) has achieved the highest score at 43,791 without augmentation with domain knowledge. Although this exceeds the average human performance, it's still much below the human world record of 1,342,100
An excellent reference for tracking state-of-the-art models is PapersWithCode, which tracks performance data of ML models.
What will the highest score of any ML model that is un-augmented with domain knowledge on Atari 2600 Montezuma's Revenge be on 2023-02-14?
This question resolves as the highest score achieved by any model that does not harness any game-specific domain knowledge on Atari 2600 Montezuma's Revenge on 2023-02-14.
Performance figures may be taken from e-prints, conference papers, peer-reviewed articles, and blog articles by reputable AI labs (including the associated code repositories). Published performance figures must be available before 2023-02-14, 11:59PM GMT to qualify.
Domain knowledge include the position of the agent, details about the room numbers, level numbers, and knowledge about the location of keys (see e.g. Ecoffet et al., 2020).
In case the relevant performance figure is given as a confidence interval, the median value will be used to resolve the question.