  Robot control using reinforcement learning has become popular, but its
learning process generally terminates halfway through an episode for safety and
time-saving reasons. This study addresses the problem of the most popular
exception handling that temporal-difference (TD) learning performs at such
termination. That is, by forcibly assuming zero value after termination,
unintentionally implicit underestimation or overestimation occurs, depending on
the reward design in the normal states. When the episode is terminated due to
task failure, the failure may be highly valued with the unintentional
overestimation, and the wrong policy may be acquired. Although this problem can
be avoided by paying attention to the reward design, it is essential in
practical use of TD learning to review the exception handling at termination.
This paper therefore proposes a method to intentionally underestimate the value
after termination to avoid learning failures due to the unintentional
overestimation. In addition, the degree of underestimation is adjusted
according to the degree of stationarity at termination, thereby preventing
excessive exploration due to the intentional underestimation. Simulations and
real robot experiments showed that the proposed method can stably obtain the
optimal policies for various tasks and reward designs.
https://youtu.be/AxXr8uFOe7M
