An Architecture for Distinguishing between Predictors and Inhibitors in Reinforcement Learning
Authors: Patrick C. Connor, Thomas P. Trappenberg
ICLR 2014

Abstract: Reinforcement learning treats each input, feature, or stimulus as having a positive or negative reward value. Some stimuli, however, negate or inhibit the values of certain other predictors (excitors) when presented with them, but are otherwise neutral. We show that both linear and non-linear value-function approximators assign inhibitory features a strong value with the opposite valence of the predictor it inhibits (i.e., inhibitor= -excitor). In one circumstance, this gives a correct prediction (i.e., excitor + inhibitor = neutral outcome). Importantly, however, value-function approximators incorrectly predict that when the inhibitor is presented alone, a negative or oppositely valenced outcome will follow whereas the inhibitor alone is actually followed by a neutral outcome. Essentially, we show that having reward value as a direct predictive target can make inhibitors indistinguishable from excitors that predict the oppositely valenced outcome. We show that this problem can be easily avoided if the reinforcement learning problem is broken into 1) a supervised learning module that predicts the positive appearance of primary reinforcements and 2) a reinforcement learning module which sums their agent-defined values.


Peer review:
[Review]:
A sincere thank you to all of the anonymous reviewers, who have collectively raised several significant issues.  While, I believe that some of these issues could be addressed with little impact to the paper, a couple of them would require substantial enough additions that we have chosen to withdraw the paper at this time.  Your feedback has been very insightful and is appreciated.

Take care,
  - Patrick Connor (and Thomas Trappenberg)