End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations

• URL: http://arxiv.org/abs/2403.12451v4
• Date: Thu, 13 Jun 2024 06:04:08 GMT
• Title: End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations
• Authors: Lirui Luo, Guoxi Zhang, Hongming Xu, Yaodong Yang, Cong Fang, Qing Li,
• Abstract summary: We present a neuro-symbolic framework for jointly learning structured states and symbolic policies. We design a pipeline to prompt GPT-4 to generate textual explanations for the learned policies and decisions. We verify the efficacy of our approach on nine Atari tasks and present GPT-generated explanations for policies and decisions.
• Score: 15.530907808235945
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neuro-symbolic reinforcement learning (NS-RL) has emerged as a promising paradigm for explainable decision-making, characterized by the interpretability of symbolic policies. NS-RL entails structured state representations for tasks with visual observations, but previous methods cannot refine the structured states with rewards due to a lack of efficiency. Accessibility also remains an issue, as extensive domain knowledge is required to interpret symbolic policies. In this paper, we present a neuro-symbolic framework for jointly learning structured states and symbolic policies, whose key idea is to distill the vision foundation model into an efficient perception module and refine it during policy learning. Moreover, we design a pipeline to prompt GPT-4 to generate textual explanations for the learned policies and decisions, significantly reducing users' cognitive load to understand the symbolic policies. We verify the efficacy of our approach on nine Atari tasks and present GPT-generated explanations for policies and decisions.

Related papers

• Efficient Symbolic Policy Learning with Differentiable Symbolic Expression [30.855457609733637]
  We propose an efficient gradient-based learning method that learns the symbolic policy from scratch in an end-to-end way. In addition, in contrast with previous symbolic policies which only work in single-task RL because of complexity, we expand ESPL on meta-RL to generate symbolic policies for unseen tasks.
  arXiv  Detail & Related papers  (2023-11-02T03:27:51Z)
• A Novel Neural-symbolic System under Statistical Relational Learning [50.747658038910565]
  We propose a general bi-level probabilistic graphical reasoning framework called GBPGR. In GBPGR, the results of symbolic reasoning are utilized to refine and correct the predictions made by the deep learning models. Our approach achieves high performance and exhibits effective generalization in both transductive and inductive tasks.
  arXiv  Detail & Related papers  (2023-09-16T09:15:37Z)
• Goal Space Abstraction in Hierarchical Reinforcement Learning via Set-Based Reachability Analysis [0.5409704301731713]
  We introduce a Feudal HRL algorithm that concurrently learns both the goal representation and a hierarchical policy. We evaluate our approach on complex navigation tasks, showing the learned representation is interpretable, transferrable and results in data efficient learning.
  arXiv  Detail & Related papers  (2023-09-14T12:39:26Z)
• Goal Space Abstraction in Hierarchical Reinforcement Learning via Reachability Analysis [0.0]
  We propose a developmental mechanism for subgoal discovery via an emergent representation that abstracts (i.e., groups together) sets of environment states. We create a HRL algorithm that gradually learns this representation along with the policies and evaluate it on navigation tasks to show the learned representation is interpretable and results in data efficiency.
  arXiv  Detail & Related papers  (2023-09-12T06:53:11Z)
• Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning [63.148199057487226]
  We propose a modular, NEuroSymbolic Textual Agent (NESTA) that combines a generic semantic generalization with a rule induction system to learn interpretable rules as policies. Our experiments show that the proposed NESTA method outperforms deep reinforcement learning-based techniques by achieving better to unseen test games and learning from fewer training interactions.
  arXiv  Detail & Related papers  (2023-07-05T23:21:05Z)
• Representation-Driven Reinforcement Learning [57.44609759155611]
  We present a representation-driven framework for reinforcement learning. By representing policies as estimates of their expected values, we leverage techniques from contextual bandits to guide exploration and exploitation. We demonstrate the effectiveness of this framework through its application to evolutionary and policy gradient-based approaches.
  arXiv  Detail & Related papers  (2023-05-31T14:59:12Z)
• Hierarchical State Abstraction Based on Structural Information Principles [70.24495170921075]
  We propose a novel mathematical Structural Information principles-based State Abstraction framework, namely SISA, from the information-theoretic perspective. SISA is a general framework that can be flexibly integrated with different representation-learning objectives to improve their performances further.
  arXiv  Detail & Related papers  (2023-04-24T11:06:52Z)
• Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach [18.38878415765146]
  We propose Explainable Reinforcement Learning (DERRL), a framework that exploits the best of both -- neural and symbolic worlds. DERRL combines relational representations and constraints from symbolic planning with deep learning to extract interpretable policies. These policies are in the form of logical rules that explain how each decision (or action) is arrived at.
  arXiv  Detail & Related papers  (2023-04-17T15:11:40Z)
• Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search [63.3745291252038]
  We propose DiffSES, a novel symbolic learning approach that discovers discrete symbolic policies. By using object-level abstractions instead of raw pixel-level inputs, DiffSES is able to leverage the simplicity and scalability advantages of symbolic expressions. Our experiments demonstrate that DiffSES is able to generate symbolic policies that are simpler and more scalable than state-of-the-art symbolic RL methods.
  arXiv  Detail & Related papers  (2022-12-30T17:50:54Z)
• Learning Symbolic Rules for Interpretable Deep Reinforcement Learning [31.29595856800344]
  We propose a Neural Symbolic Reinforcement Learning framework by introducing symbolic logic into DRL. We show that our framework has better interpretability, along with competing performance in comparison to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-03-15T09:26:00Z)
• Tree-Structured Policy based Progressive Reinforcement Learning for Temporally Language Grounding in Video [128.08590291947544]
  Temporally language grounding in untrimmed videos is a newly-raised task in video understanding. Inspired by human's coarse-to-fine decision-making paradigm, we formulate a novel Tree-Structured Policy based Progressive Reinforcement Learning framework.
  arXiv  Detail & Related papers  (2020-01-18T15:08:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.