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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Towards a Broad-Persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments.

Hung Son Nguyen1, Francisco Cruz2,3, Richard Dazeley1

  • 1School of Information Technology, Deakin University, Geelong 3220, Australia.

Sensors (Basel, Switzerland)
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces Broad-Persistent Advising (BPA) for Deep Reinforcement Learning (DeepRL) in robotics. BPA enhances learning speed by allowing agents to retain and reuse expert advice across similar states, improving efficiency.

Keywords:
broad-persistent advisingdeep reinforcement learninginteractive reinforcement learningpersistent advicereinforcement learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep Reinforcement Learning (DeepRL) is crucial for autonomous robotic behavior acquisition.
  • Deep Interactive Reinforcement Learning (DeepIRL) uses expert advice but is limited by state-specific, single-use information.
  • Current methods discard valuable learned information, leading to redundant learning processes.

Purpose of the Study:

  • To introduce Broad-Persistent Advising (BPA), an advancement over DeepIRL.
  • To enable agents to retain and reuse processed advice for similar states, not just the current one.
  • To accelerate the learning process in robotic applications.

Main Methods:

  • Developed the Broad-Persistent Advising (BPA) approach for Deep Interactive Reinforcement Learning.
  • Implemented BPA to allow general advice relevant to similar states and persistent information reuse.
  • Tested BPA in continuous robotic scenarios: cart pole balancing and robot navigation.

Main Results:

  • The BPA approach significantly increased the agent's learning speed.
  • Demonstrated up to a 37% rise in reward points, indicating faster learning.
  • Maintained the trainer's interaction count compared to standard DeepIRL.

Conclusions:

  • Broad-Persistent Advising (BPA) effectively accelerates Deep Reinforcement Learning in robotics.
  • The method improves learning efficiency by enabling persistent and generalized advice.
  • BPA offers a more robust and efficient approach to interactive learning for agents.