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Learning Intention-Aware Policies in Deep Reinforcement Learning.

T Zhao1, S Wu1, G Li1

  • 1College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300457, P.R.C. tingting@tust.edu.cn.

Neural Computation
|July 31, 2023
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Summary
This summary is machine-generated.

This study introduces intention-aware policy learning for deep reinforcement learning (DRL) agents. The new method allows agents to incorporate human-like intentions into their decision-making for improved control.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Deep reinforcement learning (DRL) agents optimize policies based on state, memory, and parameters.
  • Human decision-making incorporates intentions (e.g., speed) beyond traditional DRL factors.

Purpose of the Study:

  • To develop an intention-aware policy learning method for DRL agents.
  • To enable agents to select actions that incorporate specific intentions, mimicking human behavior.

Main Methods:

  • Formalized an intention-aware policy by integrating intention information into the policy model.
  • Optimized the policy by maximizing cumulative rewards and mutual information (MI) between intention and action.
  • Derived an efficient approximation of the MI objective for practical implementation.

Main Results:

  • Demonstrated the effectiveness of the intention-aware policy in control tasks.
  • Showcased improved agent performance in classical MuJoCo and multigoal chain walking tasks.

Conclusions:

  • The proposed intention-aware policy learning method enhances DRL agents' decision-making capabilities.
  • Incorporating intentions makes agent actions more human-like and adaptable.