  Reinforcement learning (RL) has had many successes in both "deep" and
"shallow" settings. In both cases, significant hyperparameter tuning is often
required to achieve good performance. Furthermore, when nonlinear function
approximation is used, non-stationarity in the state representation can lead to
learning instability. A variety of techniques exist to combat this --- most
notably large experience replay buffers or the use of multiple parallel actors.
These techniques come at the cost of moving away from the online RL problem as
it is traditionally formulated (i.e., a single agent learning online without
maintaining a large database of training examples). Meta-learning can
potentially help with both these issues by tuning hyperparameters online and
allowing the algorithm to more robustly adjust to non-stationarity in a
problem. This paper applies meta-gradient descent to derive a set of step-size
tuning algorithms specifically for online RL control with eligibility traces.
Our novel technique, Metatrace, makes use of an eligibility trace analogous to
methods like $TD(\lambda)$. We explore tuning both a single scalar step-size
and a separate step-size for each learned parameter. We evaluate Metatrace
first for control with linear function approximation in the classic mountain
car problem and then in a noisy, non-stationary version. Finally, we apply
Metatrace for control with nonlinear function approximation in 5 games in the
Arcade Learning Environment where we explore how it impacts learning speed and
robustness to initial step-size choice. Results show that the meta-step-size
parameter of Metatrace is easy to set, Metatrace can speed learning, and
Metatrace can allow an RL algorithm to deal with non-stationarity in the
learning task.
