Evolutionary and Temporal Insights in Neural Networks and Reinforcement Learning

The field of neural networks and reinforcement learning is shifting towards a deeper understanding of the role of evolutionary principles and temporal dynamics in learning and adaptation. Researchers are exploring how evolutionary optimization can alter the learning dynamics of artificial neural networks, enabling them to learn more efficiently and adapt to new situations. Additionally, there is a growing interest in modeling temporal symmetries and closed-loop dynamics in reinforcement learning, which can improve sample efficiency and performance in complex tasks. Noteworthy papers in this area include:

  • Time Reversal Symmetry for Efficient Robotic Manipulations in Deep Reinforcement Learning, which proposes a novel framework for exploiting temporal symmetries in robotics tasks.
  • World Models as Reference Trajectories for Rapid Motor Adaptation, which introduces a dual control framework for rapid adaptation in changing environments.
  • Maximum Total Correlation Reinforcement Learning, which promotes simple behavior throughout episodes by maximizing total correlation within trajectories.
  • Meta-reinforcement learning with minimum attention, which applies the least action principle to meta-learning and stabilization in high-dimensional nonlinear dynamics.

Sources

Evolution imposes an inductive bias that alters and accelerates learning dynamics

Learning Dynamics of RNNs in Closed-Loop Environments

Time Reversal Symmetry for Efficient Robotic Manipulations in Deep Reinforcement Learning

World Models as Reference Trajectories for Rapid Motor Adaptation

Maximum Total Correlation Reinforcement Learning

Meta-reinforcement learning with minimum attention

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