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The Role of Time Steps in Navigation
The only input was coordinates, goal coordinate, and where is the agent located right now in the coordinate system that gets established based on agents start space. The probability of correctly predicting whether an agent has been at a particular location is higher when that location is along the pathway to go from point A to point B and lower when it is on an excursion. What we found is that the fact that these are low dimensional inputs means we can actually train fairly long LSTMs. Anything performance did not saturate till a thousand steps of the past history, which is new, at least in the LSTM land.