yiconghong / thinking-vln Goto Github PK

Thanks for your amazing sharing. I am a novice to VLN, but still motivated by your ideas. I notice that it is inevitable for an agent to make mistakes, which come mainly from the mismatching of sub-instruction and observer (or location, I think you know what I mean). This issue results from many reasons, such as weak alignment or weak perception. Self-exploring which shares the same idea in RL, wants to access some unobserved information that may be undesired in this instruction but somewhere useful. But it may lack motivation for an agent to remember it since it obviously obtains nothing in this step but is activated in a much longer range. My idea is why not initially let the agent learn from easy instruction or sub-instruction and then focus on the hard one. Of course, this idea is for training a machine as a human curriculum, i.e., Curriculum Learning, and has no conflict with self-exploring. If we combine them, this idea becomes to only explore if the agent truly doesn't how to do. I believe in this way the searching space would be reduced largely (when purely exploring, the space may be large), and more likely as human behavior.