Regularizing soft decision trees

Recently, we have proposed a new decision tree family called soft decision trees where a node chooses both its left and right children with different probabilities as given by a gating function, different from a hard decision node which chooses one of the two. In this paper, we extend the original algorithm by introducing local dimension reduction via L-1 and L-2 regularization for feature selection and smoother fitting. We compare our novel approach with the standard decision tree algorithms over 27 classification data sets. We see that both regularized versions have similar generalization ability with less complexity in terms of number of nodes, where L-2 seems to work slightly better than L-1.Publisher's VersionAuthor Post Prin

Heuristic Tree Search for Detection and Decoding of Uncoded and Linear Block Coded Communication Systems

A heuristic tree search algorithm is developed for the maximum likelihood detection and decoding problem in a general communication system. We propose several "cheap" heuristic functions using constrained linear detectors and the minimum mean square errors (MMSE) detector. Even though the MMSE heuristic function does not guarantee the optimal solution, it has a negligible performance loss and provides a good complexity-performance tradeoff. For linear block coded systems, heuristic tree search is modified for soft decision decoding. High rate codes are decoded via the minimum state trellis, and low rate codes via the minimum complexity tree. Preprocessing is also discussed to further speed up the algorithms

MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees

While achieving tremendous success in various fields, existing multi-agent reinforcement learning (MARL) with a black-box neural network architecture makes decisions in an opaque manner that hinders humans from understanding the learned knowledge and how input observations influence decisions. Instead, existing interpretable approaches, such as traditional linear models and decision trees, usually suffer from weak expressivity and low accuracy. To address this apparent dichotomy between performance and interpretability, our solution, MIXing Recurrent soft decision Trees (MIXRTs), is a novel interpretable architecture that can represent explicit decision processes via the root-to-leaf path and reflect each agent's contribution to the team. Specifically, we construct a novel soft decision tree to address partial observability by leveraging the advances in recurrent neural networks, and demonstrate which features influence the decision-making process through the tree-based model. Then, based on the value decomposition framework, we linearly assign credit to each agent by explicitly mixing individual action values to estimate the joint action value using only local observations, providing new insights into how agents cooperate to accomplish the task. Theoretical analysis shows that MIXRTs guarantees the structural constraint on additivity and monotonicity in the factorization of joint action values. Evaluations on the challenging Spread and StarCraft II tasks show that MIXRTs achieves competitive performance compared to widely investigated methods and delivers more straightforward explanations of the decision processes. We explore a promising path toward developing learning algorithms with both high performance and interpretability, potentially shedding light on new interpretable paradigms for MARL