A connectionist actor-critic algorithm for faster learning and biological plausibility

We propose a novel biologically plausible actor-critic algorithm using policy gradients in order to achieve practical, model-free reinforcement learning. It does not rely on backpropagation and is the first neural actor-critic relying only on locally available information. We show it has an advantage over pure policy gradients methods for motor learning performance in the polecart problem. We are also able to closely simulate the dopaminergic signaling patterns in rats when confronted with a two cue problem, showing that local, connectionist models can effectively model the functioning of the intrinsic reward system.