Planning with Exploration: Addressing Dynamics Bottleneck in Model-based Reinforcement Learning
This addresses a key limitation in model-based reinforcement learning for improving sample efficiency in complex control problems, though it is incremental as it builds on existing exploration techniques.
The paper tackles the dynamics bottleneck dilemma in model-based reinforcement learning, where performance plateaus with more data, by identifying trajectory reward estimation error as the cause and proposing MOPE2 to enhance exploration, which alleviates the bottleneck and achieves higher sample efficiency than prior methods on continuous control tasks.
Model-based reinforcement learning (MBRL) is believed to have higher sample efficiency compared with model-free reinforcement learning (MFRL). However, MBRL is plagued by dynamics bottleneck dilemma. Dynamics bottleneck dilemma is the phenomenon that the performance of the algorithm falls into the local optimum instead of increasing when the interaction step with the environment increases, which means more data can not bring better performance. In this paper, we find that the trajectory reward estimation error is the main reason that causes dynamics bottleneck dilemma through theoretical analysis. We give an upper bound of the trajectory reward estimation error and point out that increasing the agent's exploration ability is the key to reduce trajectory reward estimation error, thereby alleviating dynamics bottleneck dilemma. Motivated by this, a model-based control method combined with exploration named MOdel-based Progressive Entropy-based Exploration (MOPE2) is proposed. We conduct experiments on several complex continuous control benchmark tasks. The results verify that MOPE2 can effectively alleviate dynamics bottleneck dilemma and have higher sample efficiency than previous MBRL and MFRL algorithms.