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Related Questions
- Can we interpret the regularization parameter's role in balancing exploration-exploitation trade-off across different instances of Gaussian Processes in the context of Gaussian Process-based reinforcement learning?
- How does increasing/decreasing the regularization parameter influence exploration-exploitation dynamics during the training and decision-making processes in GPIRL settings?
- In multi-task GPIRL, are there optimal value ranges of the regularization parameter to mitigate overfitting in separate tasks?
- Which specific learning situations necessitate a stronger/exploitational regularization approach using Gaussian processes, while for other use cases a noisier exploration-friendly version of GP optimization is required?
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