Short Answer

Purpose of Reward Decomposition in Policy Gradient

A key step in reformulating the policy gradient expression involves rewriting the total reward for a trajectory, (sum_{k=1 to T} r_k), as a sum of two components: rewards accumulated before a given timestep t (sum_{k=1 to t-1} r_k) and rewards accumulated from that timestep onward (sum_{k=t to T} r_k). Explain the primary motivation for performing this decomposition. What does this separation of rewards allow for in subsequent steps of the gradient calculation?

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Updated 2025-10-04

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