  In machine learning for sequential decision-making, an algorithmic agent
learns to interact with an environment while receiving feedback in the form of
a reward signal. However, in many unstructured real-world settings, such a
reward signal is unknown and humans cannot reliably craft a reward signal that
correctly captures desired behavior. To solve tasks in such unstructured and
open-ended environments, we present Demonstration-Inferred Preference
Reinforcement Learning (DIP-RL), an algorithm that leverages human
demonstrations in three distinct ways, including training an autoencoder,
seeding reinforcement learning (RL) training batches with demonstration data,
and inferring preferences over behaviors to learn a reward function to guide
RL. We evaluate DIP-RL in a tree-chopping task in Minecraft. Results suggest
that the method can guide an RL agent to learn a reward function that reflects
human preferences and that DIP-RL performs competitively relative to baselines.
DIP-RL is inspired by our previous work on combining demonstrations and
pairwise preferences in Minecraft, which was awarded a research prize at the
2022 NeurIPS MineRL BASALT competition, Learning from Human Feedback in
Minecraft. Example trajectory rollouts of DIP-RL and baselines are located at
https://sites.google.com/view/dip-rl.
