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From my understanding they can train the agent to accomplish a fairly complex task, by first "training" the agent to be able to accomplish simpler tasks that ar
by QasimK 9y ago
From my understanding they can train the agent to accomplish a fairly complex task, by first "training" the agent to be able to accomplish simpler tasks that are perhaps only slightly related to the final goal. If you want to learn to run, first learn the more basic skills of balancing, standing up and walking. The agent appears to decide what task it wants to try to pursue, but still receives signals about all the tasks available to it, and has a way of planning and following through on what future tasks to carry out.
I wonder if there's any way to automatically generate useful, simple goals?
- zbyte64 9y agoMaybe something akin to this: https://blog.openai.com/interpretable-machine-learning-through-teaching/ https://blog.openai.com/interpretable-machine-learning-throu... My thinking is you could train the first generation without simpler tasks but it would pick up crude skills along the way. Have the first generation generate tasks that convey the basic skills it learned and use that to teach the second generation. Edit: Or just feedback the generated tasks and not train a new neural net...
- stokasto 9y agoCo-author of the paper here. That is a good high-level summary of the approach :). Generating useful "simple"/"low-level" goals automatically indeed is an interesting avenue for pushing this further.
- craigus 9y agoHi Tobias, thanks for dropping into the thread. When this kind of software becomes mature enough that a consultant can install a robotic arm on a factory line, and quickly (several hours) train it to do the job of a factory line worker, there will be a massive economic incentive to do so. How far do you think we are from this level of maturity? What are the remaining steps required to reach that level?