Extending Environments To Measure Self-Reflection In Reinforcement Learning
This work addresses the challenge of quantifying self-reflection in AI agents, which is an incremental step in understanding agent cognition.
The paper tackles the problem of measuring self-reflection in reinforcement learning by introducing extended environments that simulate the agent's hypothetical behavior, and it shows that a simple transformation improves some standard RL agents' performance in these environments.
We consider an extended notion of reinforcement learning in which the environment can simulate the agent and base its outputs on the agent's hypothetical behavior. Since good performance usually requires paying attention to whatever things the environment's outputs are based on, we argue that for an agent to achieve on-average good performance across many such extended environments, it is necessary for the agent to self-reflect. Thus weighted-average performance over the space of all suitably well-behaved extended environments could be considered a way of measuring how self-reflective an agent is. We give examples of extended environments and introduce a simple transformation which experimentally seems to increase some standard RL agents' performance in a certain type of extended environment.