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Recent work in AI safety has highlighted that in sequential decision making,objectives are often underspecified or incomplete. This gives discretion to theacting agent to realize the stated objective in ways that may result inundesirable outcomes. We contend that to learn to act safely, a reinforcementlearning (RL) agent should include contemplation of the impact of its actionson the wellbeing and agency of others in the environment, including otheracting agents and reactive processes. We endow RL agents with the ability tocontemplate such impact by augmenting their reward based on expectation offuture return by others in the environment, providing different criteria forcharacterizing impact. We further endow these agents with the ability todifferentially factor this impact into their decision making, manifestingbehavior that ranges from self-centred to self-less, as demonstrated byexperiments in gridworld environments.

Be Considerate: Objectives, Side Effects, and Deciding How to Act