- The paper presents novel clustering methods that group RL trajectories based on their generating policies, improving data organization in offline RL.
- It introduces Policy-Guided K-means and Centroid-Attracted Autoencoder, leveraging behavior cloning and latent feature clustering for trajectory assignment.
- Empirical tests on D4RL and GridWorld show robust clustering performance, outperforming conventional baselines like VAE, DEC, and SORL.
Policy-Based Trajectory Clustering in Offline Reinforcement Learning
The paper presents a novel approach to clustering trajectories derived from offline reinforcement learning datasets, focusing specifically on the policies underlying these trajectories. The authors introduce a methodical clustering objective which leverages the relationship between KL-divergence of trajectory distributions and mixtures of policy-induced distributions. Two primary algorithmic solutions are proposed: Policy-Guided K-means (PG-Kmeans) and Centroid-Attracted Autoencoder (CAAE).
Methodological Contributions
- Formulation of Policy-Based Trajectory Clustering: The paper starts by clearly defining the problem of clustering trajectories based on their generating policies. This sets the foundation for organizing offline reinforcement learning data, potentially improving the utilization and understanding of diverse policy behaviors.
- Policy-Guided K-means: PG-Kmeans extends the classical K-means algorithm by incorporating policy induction properties. It performs clustering by iteratively training behavior cloning policies and then assigning trajectories based on the likelihood of generation by each policy. This method maintains distinct policy clusters, which facilitate clear and direct mapping from trajectory data to policy selection.
- Centroid-Attracted Autoencoder: CAAE operates under a different philosophical approach, resembling VQ-VAE frameworks, by guiding latent representations toward specific predefined codebook entries. This aims to create a robust feature space where trajectories can be grouped based on proximity to learned latent centroids.
- Theoretical Insights and Challenges: The paper establishes finite-step convergence theoretically for PG-Kmeans, mentioning inherent ambiguities due to policy-induced conflicts which might result in multiple distinct yet valid clustering solutions. This reflects a complexity often closed to combinatorial problems like K-coloring, illustrating that unique solutions may not always be achievable.
Experimental Validation
The methods were empirically tested on environments like D4RL Gym and custom GridWorld settings, showing significant effectiveness in clustering trajectories. Results were benchmarked using the Normalized Mutual Information (NMI) metric, demonstrating robust performance against conventional clustering baselines such as standard VAE, DEC, and SORL.
Implications and Future Directions
This research has substantial implications in offline RL, offering a technique to discern and harness heterogeneous policy data. The methodology could lead to improved training algorithms that exploit clustered data for refined policy learning or even semi-supervised approaches in setups with limited reward information. Academically, this advances the understanding of trajectory-based clustering by directly associating policy dynamics with trajectory patterns, proposing steps toward overcoming traditional RL dataset complexities involving distributional shift and conflicting policy representations.
Looking forward, these findings open avenues for deeper exploration into clustering strategies beyond mere trajectory aggregation. Further investigation into minimizing ambiguity within policy clustering, and expanding theoretical guarantees, can enhance applicability. Moreover, as AI looks toward larger-scale and complex systems, integrating advanced clustering methodologies into multi-agent systems or intricate simulation environments could revolutionize structured policy learning frameworks.