  In this paper, we propose \textit{ReLeTA}: Reinforcement Learning based Task
Allocation for temperature minimization. We design a new reward function and
use a new state model to facilitate optimization of reinforcement learning
algorithm. By means of the new reward function and state model, \releta is able
to effectively reduce the system peak temperature without compromising the
application performance. We implement and evaluate \releta on a real platform
in comparison with the state-of-the-art approaches. Experimental results show
\releta can reduce the average peak temperature by 4 $^{\circ}$C and the
maximum difference is up to 13 $^{\circ}$C.
