Last Week’s Work Review#
Option recognition
The choice of actions and policies based on natural language walkthrough is highly similar to “option recognition” in traditional AI study, where natural language walkthrough will help agents to make options for subpolicies selection.
When to start /end
When we want to let agent utilise walkthrough data, the agent needs to know when to execute the walkthrough.
- it needs to know what the current situation is and then know
- what we have already done
- where are we, what to execute next
- what to do next if the previous execution failed
- currently they are not explicitly handled
Reuse object recognition module
We can use go-explore’s object recognition module
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