  A variety of autonomous navigation algorithms exist that allow robots to move
around in a safe and fast manner. However, many of these algorithms require
parameter re-tuning when facing new environments. In this paper, we propose
PTDRL, a parameter-tuning strategy that adaptively selects from a fixed set of
parameters those that maximize the expected reward for a given navigation
system. Our learning strategy can be used for different environments, different
platforms, and different user preferences. Specifically, we attend to the
problem of social navigation in indoor spaces, using a classical motion
planning algorithm as our navigation system and training its parameters to
optimize its behavior. Experimental results show that PTDRL can outperform
other online parameter-tuning strategies.
