  Mobile robots are increasingly being employed for performing complex tasks in
dynamic environments. Reinforcement learning (RL) methods are recognized to be
promising for specifying such tasks in a relatively simple manner. However, the
strong dependency between the learning method and the task to learn is a
well-known problem that restricts practical implementations of RL in robotics,
often requiring major modifications of parameters and adding other techniques
for each particular task. In this paper we present a practical core
implementation of RL which enables the learning process for multiple robotic
tasks with minimal per-task tuning or none. Based on value iteration methods,
this implementation includes a novel approach for action selection, called
Q-biased softmax regression (QBIASSR), which avoids poor performance of the
learning process when the robot reaches new unexplored states. Our approach
takes advantage of the structure of the state space by attending the physical
variables involved (e.g., distances to obstacles, X,Y,{\theta} pose, etc.),
thus experienced sets of states may favor the decision-making process of
unexplored or rarely-explored states. This improvement has a relevant role in
reducing the tuning of the algorithm for particular tasks. Experiments with
real and simulated robots, performed with the software framework also
introduced here, show that our implementation is effectively able to learn
different robotic tasks without tuning the learning method. Results also
suggest that the combination of true online SARSA({\lambda}) with QBIASSR can
outperform the existing RL core algorithms in low-dimensional robotic tasks.
