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Combining Reinforcement Learning and Tensor Networks, with an Application to Dynamical Large Deviations.

Edward Gillman1,2, Dominic C Rose3, Juan P Garrahan1,2

  • 1School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, United Kingdom.

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Summary
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We introduce ACTeN, a novel framework combining tensor networks (TNs) and reinforcement learning (RL) for complex optimization. This approach efficiently solves challenging tasks like sampling rare events in stochastic models.

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Area of Science:

  • Computational Physics
  • Machine Learning
  • Statistical Mechanics

Background:

  • Dynamical optimization tasks often involve large state and action spaces, posing significant computational challenges.
  • Reinforcement learning (RL), particularly model-free actor-critic methods, offers a powerful paradigm for solving such problems.
  • Traditional function approximators in RL can struggle with the complexity and dimensionality of these large-scale systems.

Purpose of the Study:

  • To develop a novel framework integrating tensor network (TN) methods with reinforcement learning (RL) for enhanced dynamical optimization.
  • To introduce the "actor-critic with tensor networks" (ACTeN) method, leveraging TNs for policy and value function approximation.
  • To demonstrate the efficacy of ACTeN on computationally demanding tasks, including rare trajectory sampling in stochastic models.

Main Methods:

  • The study integrates tensor network (TN) methods as function approximators within the actor-critic RL algorithm.
  • The proposed "actor-critic with tensor networks" (ACTeN) method is designed to handle large and factorizable state and action spaces.
  • ACTeN is applied to sample rare trajectories in the East model of glasses and the asymmetric simple exclusion process.

Main Results:

  • The ACTeN method successfully addresses the exponentially hard task of sampling rare trajectories in complex stochastic models.
  • The framework demonstrates particular effectiveness on the asymmetric simple exclusion process, a system challenging for methods lacking detailed balance.
  • The results highlight the suitability of TNs for approximating policy and value functions in RL for large-scale dynamical systems.

Conclusions:

  • The ACTeN framework offers a promising new approach for solving challenging dynamical optimization problems by integrating TNs and RL.
  • This method shows significant potential for applications in statistical physics, particularly in simulating rare events and complex systems.
  • The integration strategy has broad implications for multi-agent RL and advancing the capabilities of existing RL algorithms.