Hi 蔷蔷 汪,
I understand that you need to know why the initial Q0 values, and the episode reward align in few applications while they do not in other applications.
The alignment of the episode’s initial Q0 value and the episode reward depends on many parameters like the complexity of the environment, hyperparameters, the neural network architecture, and the exploration strategy.
In simpler applications with straightforward environments, the critic network can accurately estimate the initial Q0 value due to the limited complexity. As a result, the initial Q0 value and the episode reward tend to align well.
However, in more complex environments, the initial Q-value estimate from the critic network may not perfectly align with the episode reward. This misalignment can be attributed to the intricacies and variability of the task.
To enhance the convergence and performance of the DDPG algorithm, it is crucial to fine-tune the hyperparameters, adjust the neural network architecture, and experiment with different exploration strategies. These optimizations can help improve the alignment between episode Q0 and episode reward, ultimately leading to enhanced learning and policy performance.
Hope this Helps!
Best regards,
Poorna.
