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Computer Science > Machine Learning

arXiv:2106.11417 (cs)
[Submitted on 21 Jun 2021]

Title:Interpretable Model-based Hierarchical Reinforcement Learning
using Inductive Logic Programming

Authors:Duo Xu, Faramarz Fekri
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    Abstract: Recently deep reinforcement learning has achieved
    tremendous success in wide ranges of applications. However, it
    notoriously lacks data-efficiency and interpretability.
    Data-efficiency is important as interacting with the environment
    is expensive. Further, interpretability can increase the
    transparency of the black-box-style deep RL models and hence gain
    trust from the users. In this work, we propose a new hierarchical
    framework via symbolic RL, leveraging a symbolic transition model
    to improve the data-efficiency and introduce the interpretability
    for learned policy. This framework consists of a high-level
    agent, a subtask solver and a symbolic transition model. Without
    assuming any prior knowledge on the state transition, we adopt
    inductive logic programming (ILP) to learn the rules of symbolic
    state transitions, introducing interpretability and making the
    learned behavior understandable to users. In empirical
    experiments, we confirmed that the proposed framework offers
    approximately between 30\% to 40\% more data efficiency over
    previous methods.

Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2106.11417 [cs.LG]
          (or arXiv:2106.11417v1 [cs.LG] for this version)

Submission history

From: Duo Xu [view email]
[v1] Mon, 21 Jun 2021 21:30:08 UTC (5,108 KB)
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