  As reinforcement learning (RL) scales to solve increasingly complex tasks,
interest continues to grow in the fields of AI safety and machine ethics. As a
contribution to these fields, this paper introduces an extension to Deep
Q-Networks (DQNs), called Empathic DQN, that is loosely inspired both by
empathy and the golden rule ("Do unto others as you would have them do unto
you"). Empathic DQN aims to help mitigate negative side effects to other agents
resulting from myopic goal-directed behavior. We assume a setting where a
learning agent coexists with other independent agents (who receive unknown
rewards), where some types of reward (e.g. negative rewards from physical harm)
may generalize across agents. Empathic DQN combines the typical (self-centered)
value with the estimated value of other agents, by imagining (by its own
standards) the value of it being in the other's situation (by considering
constructed states where both agents are swapped). Proof-of-concept results in
two gridworld environments highlight the approach's potential to decrease
collateral harms. While extending Empathic DQN to complex environments is
non-trivial, we believe that this first step highlights the potential of
bridge-work between machine ethics and RL to contribute useful priors for
norm-abiding RL agents.

Machine Learning;Artificial Intelligence;Neural and Evolutionary Computing