Robots Learn Many Goals: Tough New Tasks to Test Smart Machines
This is about making everyday machines try harder, and sometimes fail, so they learn better.
Engineers made a set of hard puzzles where robots push, slide and pick up things, and even move objects inside a hand — these tasks look simple but they are tricky.
Each task tells the machine what to aim for, so it practices different goals at once, and rewards show up only when success happens, so it can be quiet and sparse.
Some trials give almost no hints, that's the point, the machine must figure out a way.
The toolkit links to common software so others can try same challenges on real hardware or in sims, and many teams can test ideas faster.
The second part offers clear, simple research ideas for making learning smarter by using past tries more wisely.
These ideas hope to help robots learn from mistakes, get better with little feedback, and do tasks that feel like real life, even when it is very hard and sometimes messy.
Read article comprehensive review in Paperium.net:
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Requestfor Research
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