Explaining an Agent's Future Beliefs through Temporally Decomposing Future Reward Estimators

TL;DR

This work alters an agent's future reward estimator to predict their next N expected rewards, referred to as Temporal Reward Decomposition (TRD), and shows that DQN agents trained on Atari environments can be efficiently retrained to incorporate TRD with minimal impact on performance.

Abstract

Future reward estimation is a core component of reinforcement learning agents; i.e., Q-value and state-value functions, predicting an agent's sum of future rewards. Their scalar output, however, obfuscates when or what individual future rewards an agent may expect to receive. We address this by modifying an agent's future reward estimator to predict their next N expected rewards, referred to as Temporal Reward Decomposition (TRD). This unlocks novel explanations of agent behaviour. Through TRD we can: estimate when an agent may expect to receive a reward, the value of the reward and the agent's confidence in receiving it; measure an input feature's temporal importance to the agent's action decisions; and predict the influence of different actions on future rewards. Furthermore, we show that DQN agents trained on Atari environments can be efficiently retrained to incorporate TRD with minimal impact on performance.