Demystifying Linear MDPs and Novel Dynamics Aggregation Framework
This addresses scalability issues in reinforcement learning for environments with hierarchical structures, offering a provably efficient method with concrete regret bounds.
The paper tackles the limitation of linear MDPs where feature dimension scales with state space size, proposing a dynamics aggregation framework and a hierarchical reinforcement learning algorithm that achieves a regret of ˜O(d_ψ^{3/2} H^{3/2} √(NT)), showing improvement over LSVI-UCB under conditions common in real-world environments.
In this work, we prove that, in linear MDPs, the feature dimension $d$ is lower bounded by $S/U$ in order to aptly represent transition probabilities, where $S$ is the size of the state space and $U$ is the maximum size of directly reachable states. Hence, $d$ can still scale with $S$ depending on the direct reachability of the environment. To address this limitation of linear MDPs, we propose a novel structural aggregation framework based on dynamics, named as the "dynamics aggregation". For this newly proposed framework, we design a provably efficient hierarchical reinforcement learning algorithm in linear function approximation that leverages aggregated sub-structures. Our proposed algorithm exhibits statistical efficiency, achieving a regret of $ \tilde{O} ( d_ψ^{3/2} H^{3/2}\sqrt{ N T} )$, where $d_ψ$ represents the feature dimension of aggregated subMDPs and $N$ signifies the number of aggregated subMDPs. We establish that the condition $d_ψ^3 N \ll d^{3}$ is readily met in most real-world environments with hierarchical structures, enabling a substantial improvement in the regret bound compared to LSVI-UCB, which enjoys a regret of $ \tilde{O} (d^{3/2} H^{3/2} \sqrt{ T})$. To the best of our knowledge, this work presents the first HRL algorithm with linear function approximation that offers provable guarantees.