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Well actually, learning without negative reenforcement is possible. Experiments with pigeons over half a century ago showed that this was effective. See for exa
by todd8 10y ago
Well actually, learning without negative reenforcement is possible. Experiments with pigeons over half a century ago showed that this was effective. See for example [1] and [2].
Interestingly, learning under training with inconsistent rewards (i.e. rewarding correct responses only some of the time) results in training that is stronger in the sense that birds after the training phase will keep up the trained behavior longer. It's like they don't get discouraged as quickly when they sometimes don't get the rewards they expect.
I'm not sure what the implications are for machine learning, but especially for neural net learning it's interesting to consider biological models for learning.
[1] Errorless Learning of a Conditional Temporal Discrimination https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3014774/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3014774/
[2] Errorless Learning https://en.m.wikipedia.org/wiki/Errorless_learning https://en.m.wikipedia.org/wiki/Errorless_learning
- rahimnathwani 10y agoIn the pigeon example, there was no negative reinforcement, but there were negative examples used during training. They first trained the pigeons to peck bright red buttons. But introducing bright green buttons would have had the pigeons pecking those, too. So they first trained them with negative examples that were even more different from the ones they'd seen: dimly-lit green buttons that were lit for only a short time. The dimly-lit green buttons were the negative examples here, just like dogs would be if you were training a machine to recognise cats.