Tuning for the Unknown: Revisiting Evaluation Strategies for Lifelong RL

In continual or lifelong reinforcement learning access to the environment should be limited. If we aspire to design algorithms that can run for long-periods of time, continually adapting to new, unexpected situations then we must be willing to deploy our agents without tuning their hyperparameters over the agent's entire lifetime. The standard practice in deep RL -- and even continual RL -- is to assume unfettered access to deployment environment for the full lifetime of the agent. This paper explores the notion that progress in lifelong RL research has been held back by inappropriate empirical methodologies. In this paper we propose a new approach for tuning and evaluating lifelong RL agents where only one percent of the experiment data can be used for hyperparameter tuning. We then conduct an empirical study of DQN and Soft Actor Critic across a variety of continuing and non-stationary domains. We find both methods generally perform poorly when restricted to one-percent tuning, whereas several algorithmic mitigations designed to maintain network plasticity perform surprising well. In addition, we find that properties designed to measure the network's ability to learn continually indeed correlate with performance under one-percent tuning.