How Actor-Critic Methods Use Two Neural Networks to Train Smarter AI Agents

Actor-critic reinforcement learning trains two components simultaneously: a policy network (actor) that selects actions and a value network (critic) that estimates expected future rewards. The critic reduces the high variance of raw policy gradient estimates by computing an advantage signal — measuring whether an outcome was better or worse than expected. This advantage is calculated as a temporal-difference error, comparing actual rewards plus discounted future value against the critic's current state-value prediction. Generalised Advantage Estimation (GAE), introduced by Schulman et al. in 2015, interpolates between low-variance but biased one-step TD estimates and high-variance but unbiased Monte Carlo returns using a single tunable parameter. In practice, both networks often share a common trunk with two output heads, reducing compute but coupling their gradients in ways that introduce specific training trade-offs.