Reinforcement learning (RL) research typically develops algorithms for helping an RL agent best achieve its goals|however they came to be dened|while ignoring the relationship of those goals to the goals of the agent designer. We extend agent design to include the meta-optimization problem of selecting internal agent goals (rewards) which optimize the designer’s goals. Our claim is that well-designed internal rewards can help improve the performance of RL agents which are computationally bounded in some way (as practical agents are). We present a formal framework for understanding both bounded agents and the meta-optimization problem, and we empirically demonstrate several instances of common agent bounds being mitigated by general internal reward functions.
['Jonathan Sorg', 'Satinder P. Singh', 'Richard L. Lewis']
Internal Rewards Mitigate Agent Boundedness