  The current reward learning from human preferences could be used to resolve
complex reinforcement learning (RL) tasks without access to a reward function
by defining a single fixed preference between pairs of trajectory segments.
However, the judgement of preferences between trajectories is not dynamic and
still requires human input over thousands of iterations. In this study, we
proposed a weak human preference supervision framework, for which we developed
a human preference scaling model that naturally reflects the human perception
of the degree of weak choices between trajectories and established a
human-demonstration estimator via supervised learning to generate the predicted
preferences for reducing the number of human inputs. The proposed weak human
preference supervision framework can effectively solve complex RL tasks and
achieve higher cumulative rewards in simulated robot locomotion -- MuJoCo games
-- relative to the single fixed human preferences. Furthermore, our established
human-demonstration estimator requires human feedback only for less than 0.01\%
of the agent's interactions with the environment and significantly reduces the
cost of human inputs by up to 30\% compared with the existing approaches. To
present the flexibility of our approach, we released a video
(https://youtu.be/jQPe1OILT0M) showing comparisons of the behaviours of agents
trained on different types of human input. We believe that our naturally
inspired human preferences with weakly supervised learning are beneficial for
precise reward learning and can be applied to state-of-the-art RL systems, such
as human-autonomy teaming systems.
