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Continuous Control Monte Carlo Tree Search Informed by Multiple Experts.

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    This summary is machine-generated.

    This study introduces a novel Monte Carlo tree search (MCTS) controller for efficient 3D character control. The algorithm enables rapid development of complex locomotion skills for 3D humanoids with minimal computation.

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    Area of Science:

    • Robotics
    • Computer Science
    • Artificial Intelligence

    Background:

    • Efficient algorithms for 3D character control in continuous settings are an ongoing challenge.
    • Recent advancements in machine learning have not fully addressed this problem.

    Purpose of the Study:

    • To present a novel sampling-based model-predictive controller using Monte Carlo Tree Search (MCTS).
    • To enable rapid development of complex 3D character control skills, including locomotion.
    • To demonstrate robustness against external disturbances and adaptability to varying character topologies.

    Main Methods:

    • Utilized a Monte Carlo Tree Search (MCTS) framework for control.
    • Integrated information from multiple sources, including two machine learning models.
    • Extended neural networks to represent stochastic policies.
    • Developed a new method for combining information from diverse sources with minimal loss.

    Main Results:

    • Achieved rapid development of complex skills like 3D humanoid locomotion in under a million simulation steps.
    • Demonstrated robust locomotion under disturbances such as projectile impacts and changing target directions.
    • Showcased control of 3D characters with varying topologies.
    • The algorithm learns in near real-time and can operate in real-time after learning.

    Conclusions:

    • The proposed MCTS-based controller offers a robust and user-friendly solution for 3D character control.
    • The approach facilitates efficient skill acquisition for complex character behaviors.
    • The method is effective for diverse character models and challenging environmental conditions.