Qtargets

Qtargets is a term used in reinforcement learning to refer to the target values used when updating Q-values in algorithms such as Q-learning and its extensions. These targets represent the estimated future return that the learning process aims to achieve from a given state-action pair.

In the standard Q-learning update, the Q-target for a transition (s, a, r, s') is y = r + gamma * max over all actions a' of Q(s', a'). The temporal-difference error is then delta = y - Q(s, a), and the Q-function is updated toward the target y using a learning rate. This mechanism drives the Q-values toward better estimates of expected returns under the policy implied by the greedy action selection.

In deep Q-learning, a separate target network with fixed parameters theta- is commonly used to compute the Q-target, with y = r + gamma * max_{a'} Q(s', a'; theta-). The target network is updated periodically or softly to stabilize learning, preventing rapid oscillations in the target values.

Variants and extensions often modify how targets are computed. Double Q-learning uses two estimators to reduce overestimation bias, decoupling action selection from evaluation. Other approaches alter targets by employing expected-value targets, distributional targets, or alternate loss formulations, each affecting convergence and stability.

Relation to other methods: In SARSA, the target is y = r + gamma * Q(s', a'), using the actual next action taken instead of the maximum, which changes the target dynamics and learning behavior.

Applications of Q-targets span game playing, robotics, and other domains involving sequential decision making, where stable and accurate value estimates are essential for effective policy learning.