Why is my agent stuck on the same action in my Twin Delayed Deep Deterministic Policy Gradient (TD3) program?

I've been tirelessly converting a reinforcement learning program from Python to JavaScript using TensorFlow.js that is running Twin Delayed Deep Deterministic Policy Gradient (TD3). I'm just trying to make a basic blueprint for myself and the community to use. I've put in a lot of work and I feel like it should basically be complete, but I can't find out why the agent keeps (very quickly) converging on a single action selection (even though it's being penalized for doing so, even testing at 300 iterations). I checked the loss variables and they seem to be working as expected. Although I'm great with JavaScript and have a solid understanding of how everything works, I'm not a professional with Python or TensorFlow.js (yet). I need some people with experience in the following areas to review my code: Reinforcement Learning, TD3 (or DDPG), Tensorflow.JS. Everything is on GitHub at https://github.com/CloudZero2049/TD3-TensorFlowJS All the info about the project is in the README. TD3script.js is the only one to worry about, there's also one without comments. I have been reviewing the code and math extensively everything seems to be working properly. But when testing the agent quickly settles on a single bad action even though it's being penalized. I tried 300 iterations, 200 warmup, 100 batch_size to see if it was a time issue without a change. I feel confident in the structure and math but something is clearly wrong. After going over the code so many times and making improvement…