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Use Multi-agent Twin Delayed Deep Deterministic Policy Gradient(TD3) algorithm to find reasonable paths for ships
I'm sorry to bother you, but I encountered the following problem while learning the code you created: 1. No matter how I modify the reward function, the agent's network updates and starts spinning in place; 2. The training process was originally normal, but after a certain number of times, it suddenly converged at a very low reward value, resulting in poor training effectiveness. I hope you can provide me with some ideas to solve the problem in your free time. Thank you
Hello, as a newcomer to reinforcement learning, I have some questions I would like to ask.
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