  A biologically plausible method for training an Artificial Neural Network
(ANN) involves treating each unit as a stochastic Reinforcement Learning (RL)
agent, thereby considering the network as a team of agents. Consequently, all
units can learn via REINFORCE, a local learning rule modulated by a global
reward signal, which aligns more closely with biologically observed forms of
synaptic plasticity. Nevertheless, this learning method is often slow and
scales poorly with network size due to inefficient structural credit
assignment, since a single reward signal is broadcast to all units without
considering individual contributions. Weight Maximization, a proposed solution,
replaces a unit's reward signal with the norm of its outgoing weight, thereby
allowing each hidden unit to maximize the norm of the outgoing weight instead
of the global reward signal. In this research report, we analyze the
theoretical properties of Weight Maximization and propose a variant, Unbiased
Weight Maximization. This new approach provides an unbiased learning rule that
increases learning speed and improves asymptotic performance. Notably, to our
knowledge, this is the first learning rule for a network of Bernoulli-logistic
units that is unbiased and scales well with the number of network's units in
terms of learning speed.
