Local prediction-learning enables neural networks to plan
The capability to plan a sequence of actions toward a given goal is a
cornerstone of higher cognitive function. But compelling models for planning
in the brain, or more generally in any type of neural network, are missing.
We present a simple model for planning in neural networks, the Cognitive Map
Learner (CML), that can achieve high performance on a variety of tasks by
learning a cognitive map of the environment. The way how the CML constructs a
cognitive map is based on a fundamental insight from neuroscience:
Observations from the environment acquire meaning for the organism primarily
through performing actions that change them. The CML also provides a viable
alternative to reinforcement learning in robotics, since it learns faster and
becomes more flexible, due to its task-agnostic design principles. The design
of the CML is also of interest from the perspective of the relationship
between self-attention networks (Transformers) and neural networks, since it
combines attractive features of both.
Reference: C. Stoeckl and W.Maass.
Local prediction-learning enables neural networks to plan.