Return

reinforcement-learning

Definition

Return

Let $(s_t,a_t,r_t{)}_{t\ge 0}$ be a trajectory sampled under policy $\pi$. The return from timestep $t$ is the discounted sum of future rewards:

$$G_t=\sum _{k=0}^{\infty }{\gamma }^{k}R(s_{t+k},a_{t+k})$$

where $\gamma \in [0,1]$ is the discount factor.