  Sparse rewards present a difficult problem in reinforcement learning and may
be inevitable in certain domains with complex dynamics such as real-world
robotics. Hindsight Experience Replay (HER) is a recent replay memory
development that allows agents to learn in sparse settings by altering memories
to show them as successful even though they may not be. While, empirically, HER
has shown some success, it does not provide guarantees around the makeup of
samples drawn from an agent's replay memory. This may result in minibatches
that contain only memories with zero-valued rewards or agents learning an
undesirable policy that completes HER-adjusted goals instead of the actual
goal.
  In this paper, we introduce Or Your Money Back (OYMB), a replay memory
sampler designed to work with HER. OYMB improves training efficiency in sparse
settings by providing a direct interface to the agent's replay memory that
allows for control over minibatch makeup, as well as a preferential lookup
scheme that prioritizes real-goal memories before HER-adjusted memories. We
test our approach on five tasks across three unique environments. Our results
show that using HER in combination with OYMB outperforms using HER alone and
leads to agents that learn to complete the real goal more quickly.
