  Temporal-Difference (TD) learning is a standard and very successful
reinforcement learning approach, at the core of both algorithms that learn the
value of a given policy, as well as algorithms which learn how to improve
policies. TD-learning with eligibility traces provides a way to do temporal
credit assignment, i.e. decide which portion of a reward should be assigned to
predecessor states that occurred at different previous times, controlled by a
parameter $\lambda$. However, tuning this parameter can be time-consuming, and
not tuning it can lead to inefficient learning. To improve the sample
efficiency of TD-learning, we propose a meta-learning method for adjusting the
eligibility trace parameter, in a state-dependent manner. The adaptation is
achieved with the help of auxiliary learners that learn distributional
information about the update targets online, incurring roughly the same
computational complexity per step as the usual value learner. Our approach can
be used both in on-policy and off-policy learning. We prove that, under some
assumptions, the proposed method improves the overall quality of the update
targets, by minimizing the overall target error. This method can be viewed as a
plugin which can also be used to assist prediction with function approximation
by meta-learning feature (observation)-based $\lambda$ online, or even in the
control case to assist policy improvement. Our empirical evaluation
demonstrates significant performance improvements, as well as improved
robustness of the proposed algorithm to learning rate variation.
