A naive brute force algorithm would take a lot longer to complete some of these levels.
Let's say it takes 45 seconds to complete a level. That's 45 * (60 / 12) = 225 moves. The size of the action space is 14, so you'd be looking at 14 ^ 255 (give or take a few orders of magnitude) trajectories before finding the solution.
To brute force in a reasonable a time, you would have to look at the environment and have the algorithm iterate through all trajectories in a clever way. For instance, you may choose to only try trajectories that are constantly moving right. This strategy may find a solution to 1-1 fairly quick, but this does not generalize to other levels, especially ones that requires backtracking or waiting.
You'd have to design a pretty gnarly algorithm for it to beat 1-1, 1-4 and 2-2. This gets even more complicated if you bring in other environments: the original paper also trained on Montezuma's Revenge, Private Eye, Venture, Freeway and Gravitar
Let's say it takes 45 seconds to complete a level. That's 45 * (60 / 12) = 225 moves. The size of the action space is 14, so you'd be looking at 14 ^ 255 (give or take a few orders of magnitude) trajectories before finding the solution.
To brute force in a reasonable a time, you would have to look at the environment and have the algorithm iterate through all trajectories in a clever way. For instance, you may choose to only try trajectories that are constantly moving right. This strategy may find a solution to 1-1 fairly quick, but this does not generalize to other levels, especially ones that requires backtracking or waiting.
You'd have to design a pretty gnarly algorithm for it to beat 1-1, 1-4 and 2-2. This gets even more complicated if you bring in other environments: the original paper also trained on Montezuma's Revenge, Private Eye, Venture, Freeway and Gravitar