Hindsight policy gradients

Understand

A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy.

• In addition to their potential to generalize desirable behavior to unseen goals, such policies may also enable higher-level planning based on subgoals.
• In sparse-reward environments, the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended appears crucial to enable sample efficient learning.
• However, reinforcement learning agents have only recently been endowed with such capacity for hindsight.