  Online reinforcement learning agents are currently able to process an
increasing amount of data by converting it into a higher order value functions.
This expansion of the information collected from the environment increases the
agent's state space enabling it to scale up to a more complex problems but also
increases the risk of forgetting by learning on redundant or conflicting data.
To improve the approximation of a large amount of data, a random mini-batch of
the past experiences that are stored in the replay memory buffer is often
replayed at each learning step. The proposed work takes inspiration from a
biological mechanism which act as a protective layer of human brain higher
cognitive functions: active memory consolidation mitigates the effect of
forgetting of previous memories by dynamically processing the new ones. The
similar dynamics are implemented by a proposed augmented memory replay AMR
capable of optimizing the replay of the experiences from the agent's memory
structure by altering or augmenting their relevance. Experimental results show
that an evolved AMR augmentation function capable of increasing the
significance of the specific memories is able to further increase the stability
and convergence speed of the learning algorithms dealing with the complexity of
continuous action domains.
