Faster Learning with a Team of Reinforcement Learning Agents

*Stephen Chung*, and Andrew Barto

*Reinforcement Learning and Decision Making*, 2022

Though backpropagation underlies nearly all deep learning algorithms, it is generally regarded as being biologically implausible. An alternative way of training an artificial neural network is through making each unit stochastic and treating each unit as a reinforcement learning agent, and thus the network is considered as a team of agents. As such, all units can learn via REINFORCE, a local learning rule modulated by a global reward signal that is more consistent with biologically observed forms of synaptic plasticity. However, this learning method suffers from high variance and thus the low speed of learning. The high variance stems from the lack of effective structural credit assignment. This paper reviews two recently proposed algorithms to facilitate structural credit assignment when all units learn via REINFORCE, namely MAP Propagation and Weight Maximization. In MAP Propagation an energy function of the network is minimized before applying REINFORCE, such that activities of hidden units are more consistent with the activities of output units. In Weight Maximization the global reward signal to each hidden unit is replaced with the change in the squared L2 norm of the vector of the unit’s outgoing weights, such that each hidden unit is trying to maximize the norm of its outgoing weights instead of the external reward. Experiments show that both algorithms can learn significantly faster than a network of units learning via REINFORCE, and have a comparable speed to backpropagation when applied in standard reinforcement learning tasks. In contrast to backpropagation, both algorithms retain certain biologically plausible properties of REINFORCE, such as having local learning rules and the ability to be computed asynchronously. Therefore these algorithms may offer insights for understanding possible mechanisms of structural credit assignment in biological neural systems.