Neurocontrol for fixed-length trajectories in environments with soft barriers.

Area of Science:

• Neuroscience
• Machine Learning
• Reinforcement Learning

Background:

• Analytic policy gradient with back-propagation through time is used for training simulated agents.
• Terminal barriers in environments can cause learning to get stuck in local minima or oscillating limit cycles.

Purpose of the Study:

• To address learning challenges in neurocontrol problems with terminal barriers.
• To propose and evaluate methods for improving agent training stability and performance.

Main Methods:

• Implementing fixed-length trajectories and soft barriers with penalty costs.
• Investigating the impact of soft barriers on learning gradients.
• Applying gradient clipping and smooth truncation of gradients.

Main Results:

• Soft barriers can cause exploding learning gradients, often at inappropriate trajectory points.
• Exploding gradients combined with adaptive optimizers can halt learning.
• Gradient clipping and smooth truncation effectively mitigate exploding gradients and improve learning focus.

Conclusions:

• Soft barriers offer a way to avoid local minima but introduce gradient issues.
• Careful gradient management techniques are crucial for stable and effective neurocontrol training.
• The proposed methods enable agents to learn more effectively by focusing on relevant trajectory parts.