Be Considerate: Objectives, Side Effects, and Deciding How to Act
This work addresses AI safety concerns for reinforcement learning systems by introducing a method to mitigate undesirable outcomes from incomplete objectives, though it is incremental as it builds on existing safety frameworks.
The paper tackles the problem of AI safety in sequential decision making by proposing that reinforcement learning agents should consider the impact of their actions on others, and demonstrates this through experiments in gridworld environments where agents exhibit behaviors ranging from self-centered to self-less.
Recent work in AI safety has highlighted that in sequential decision making, objectives are often underspecified or incomplete. This gives discretion to the acting agent to realize the stated objective in ways that may result in undesirable outcomes. We contend that to learn to act safely, a reinforcement learning (RL) agent should include contemplation of the impact of its actions on the wellbeing and agency of others in the environment, including other acting agents and reactive processes. We endow RL agents with the ability to contemplate such impact by augmenting their reward based on expectation of future return by others in the environment, providing different criteria for characterizing impact. We further endow these agents with the ability to differentially factor this impact into their decision making, manifesting behavior that ranges from self-centred to self-less, as demonstrated by experiments in gridworld environments.