Comments (3)
My guess is that this is inherently due to the very nature of Gaussian Processes. GPs keeps all the data in memory that will then be used to do a prediction. The more you run pilco the more samples will be collected and thus the prediction time will increase. If your familiar with the big O notation, gp prediction time is O(n^3) where n is the number of samples. There is some research going on to reduce this ( sparse gaussians etc) but in the overall, the more samples you have, the longer the policy optimization will be.
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I think I agree with @NicolayP. You might want to use sparse gaussian processes, which are already implemented in PILCO.
Let me know if this helps with your problem.
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Thank you for your help @nrontsis @NicolayP, I will try the sparse gaussian processes PILCO
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Related Issues (20)
- Computation of cross-covariance of state and action
- Question about MGPR.
- Error with cloudpickle
- Reference for predicting with uncertain inputs with SMGPR HOT 1
- Gradient based policy optimisation. HOT 4
- SMGPR : the induced points are different for each model HOT 1
- calculate_factorizations question HOT 1
- Cost for trajectory following HOT 3
- Cholesky decomposition was not successful. The input might not be valid. HOT 2
- [BUG] mountain_car.py fails due to missing import
- What is the V for in the predict_given_factorizations HOT 1
- installation: issue with gast, tensorflow HOT 6
- How do you save your trained model? HOT 2
- Could you please share exact version of some dependency packages
- Performance issue in the definition of create_models, pilco/controllers.py(P1)
- AttributeError: 'Parameter' object has no attribute 'value'
- NotImplementedError: Cannot convert a symbolic (graph mode) `DeferredTensor` to a numpy array. HOT 2
- Is squash_sin() right? HOT 1
- Bugs in model update? HOT 1
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