Mind the Model, Not the Agent: The Primacy Bias in Model-based RL
This addresses a performance issue in MBRL algorithms for researchers and practitioners, offering a simple technique to enhance learning stability, though it is incremental as it builds on known bias mitigation in model-free RL.
The paper tackles the primacy bias in model-based reinforcement learning (MBRL), where agents overfit early data, and finds that resetting the world model instead of the agent's parameters significantly alleviates this bias and improves performance on tasks like MuJoCo, DeepMind Control Suite, and Atari 100k.
The primacy bias in model-free reinforcement learning (MFRL), which refers to the agent's tendency to overfit early data and lose the ability to learn from new data, can significantly decrease the performance of MFRL algorithms. Previous studies have shown that employing simple techniques, such as resetting the agent's parameters, can substantially alleviate the primacy bias in MFRL. However, the primacy bias in model-based reinforcement learning (MBRL) remains unexplored. In this work, we focus on investigating the primacy bias in MBRL. We begin by observing that resetting the agent's parameters harms its performance in the context of MBRL. We further find that the primacy bias in MBRL is more closely related to the primacy bias of the world model instead of the primacy bias of the agent. Based on this finding, we propose \textit{world model resetting}, a simple yet effective technique to alleviate the primacy bias in MBRL. We apply our method to two different MBRL algorithms, MBPO and DreamerV2. We validate the effectiveness of our method on multiple continuous control tasks on MuJoCo and DeepMind Control Suite, as well as discrete control tasks on Atari 100k benchmark. The experimental results show that \textit{world model resetting} can significantly alleviate the primacy bias in the model-based setting and improve the algorithm's performance. We also give a guide on how to perform \textit{world model resetting} effectively.