  Using neural networks in the reinforcement learning (RL) framework has
achieved notable successes. Yet, neural networks tend to forget what they
learned in the past, especially when they learn online and fully incrementally,
a setting in which the weights are updated after each sample is received and
the sample is then discarded. Under this setting, an update can lead to overly
global generalization by changing too many weights. The global generalization
interferes with what was previously learned and deteriorates performance, a
phenomenon known as catastrophic interference. Many previous works use
mechanisms such as experience replay (ER) buffers to mitigate interference by
performing minibatch updates, ensuring the data distribution is approximately
independent-and-identically-distributed (i.i.d.). But using ER would become
infeasible in terms of memory as problem complexity increases. Thus, it is
crucial to look for more memory-efficient alternatives. Interference can be
averted if we replace global updates with more local ones, so only weights
responsible for the observed data sample are updated. In this work, we propose
the use of dynamic self-organizing map (DSOM) with neural networks to induce
such locality in the updates without ER buffers. Our method learns a DSOM to
produce a mask to reweigh each hidden unit's output, modulating its degree of
use. It prevents interference by replacing global updates with local ones,
conditioned on the agent's state. We validate our method on standard RL
benchmarks including Mountain Car and Lunar Lander, where existing methods
often fail to learn without ER. Empirically, we show that our online and fully
incremental method is on par with and in some cases, better than
state-of-the-art in terms of final performance and learning speed. We provide
visualizations and quantitative measures to show that our method indeed
mitigates interference.
