  Existing game AI research mainly focuses on enhancing agents' abilities to
win games, but this does not inherently make humans have a better experience
when collaborating with these agents. For example, agents may dominate the
collaboration and exhibit unintended or detrimental behaviors, leading to poor
experiences for their human partners. In other words, most game AI agents are
modeled in a "self-centered" manner. In this paper, we propose a
"human-centered" modeling scheme for collaborative agents that aims to enhance
the experience of humans. Specifically, we model the experience of humans as
the goals they expect to achieve during the task. We expect that agents should
learn to enhance the extent to which humans achieve these goals while
maintaining agents' original abilities (e.g., winning games). To achieve this,
we propose the Reinforcement Learning from Human Gain (RLHG) approach. The RLHG
approach introduces a "baseline", which corresponds to the extent to which
humans primitively achieve their goals, and encourages agents to learn
behaviors that can effectively enhance humans in achieving their goals better.
We evaluate the RLHG agent in the popular Multi-player Online Battle Arena
(MOBA) game, Honor of Kings, by conducting real-world human-agent tests. Both
objective performance and subjective preference results show that the RLHG
agent provides participants better gaming experience.
