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Decomposing user-defined tasks in a reinforcement learning setup using TextWorld.

Thanos Petsanis1, Christoforos Keroglou1, Athanasios Ch Kapoutsis2

  • 1School of Engineering, Department of Electrical and Computer Engineering, Democritus University of Thrace (DUTH), Xanthi, Greece.

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Summary
This summary is machine-generated.

This study introduces hierarchical reinforcement learning (HRL) to simplify complex tasks for autonomous agents. This approach enhances agent training by disentangling actions and enabling dense rewards, improving overall performance.

Keywords:
autonomous agentsformal methods in robotics and automationhierarchical reinforcement learningreinforcement learningtask and motion planning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Complex tasks pose significant challenges for training autonomous agents.
  • Sparse reward functions in simulated environments often hinder effective learning.
  • Hierarchical Reinforcement Learning (HRL) offers a potential solution for task decomposition.

Purpose of the Study:

  • To propose and evaluate a novel hierarchical reinforcement learning (HRL) method for autonomous agent training.
  • To demonstrate the benefits of task decomposition in improving agent learning efficiency.
  • To leverage high-level abstractions for enhanced reward function design.

Main Methods:

  • Implementation of a hierarchical reinforcement learning (HRL) framework using TextWorld and MiniGrid Python environments.
  • Utilizing MiniGrid for 2D environment simulation and TextWorld for high-level task abstraction.
  • Designing a dense reward function for the lower-level environment based on task abstraction.
  • Employing formal methods to verify the solution-finding capabilities of the proposed algorithm.

Main Results:

  • The proposed HRL method successfully decomposes complex tasks into manageable sub-tasks.
  • Training on the TextWorld abstraction disentangles manipulation and navigation, simplifying the learning process.
  • The use of a dense reward function significantly improves agent training performance compared to sparse rewards.
  • Formal methods confirmed that the algorithm is capable of deriving solutions.

Conclusions:

  • Hierarchical reinforcement learning (HRL) with task abstraction provides an effective strategy for training autonomous agents.
  • The integration of TextWorld and MiniGrid facilitates a practical and efficient implementation of HRL.
  • Disentangling actions and employing dense rewards are key factors in enhancing agent performance in simulated environments.