  Recently, neuro-inspired episodic control (EC) methods have been developed to
overcome the data-inefficiency of standard deep reinforcement learning
approaches. Using non-/semi-parametric models to estimate the value function,
they learn rapidly, retrieving cached values from similar past states. In
realistic scenarios, with limited resources and noisy data, maintaining
meaningful representations in memory is essential to speed up the learning and
avoid catastrophic forgetting. Unfortunately, EC methods have a large space and
time complexity. We investigate different solutions to these problems based on
prioritising and ranking stored states, as well as online clustering
techniques. We also propose a new dynamic online k-means algorithm that is both
computationally-efficient and yields significantly better performance at
smaller memory sizes; we validate this approach on classic reinforcement
learning environments and Atari games.

Machine Learning;Neural and Evolutionary Computing;Machine Learning