Comments (2)
ctallec
commented on August 20, 2024
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The screen output corresponds to various information regarding CMAES optimization procedure. You can find more detailed information in the documentation of the CMA package: https://github.com/CMA-ES/pycma.
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With a single worker and a single GPU, it's going to take a really long time to train. For reference, our own experiments took about a day, with about 30 workers and 4 P100 gpus.
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CMAES is a black box optimization algorithm who is normally best suited to optimize deterministic functions. In our case, the return obtained by the agent is stochastic (notably due to the randomized road tracks used during training). To decrease the amount of stochasticity of the optimized function, the return obtained is averaged on n-samples rollouts. Besides, CMA is a population based black box optimization method: at each optimization step, CMA maintains a population of candidate solutions to the optimization problem, and these candidates evolve during optimization. The population size thus refers to the number of candidates that CMA maintains during optimization.
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Fairasname
commented on August 20, 2024
Alright, now is much more clear!
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Related Issues (20)
- Shouldn't this be outside the for loop? HOT 3
- Data generation script: No module named 'utils' HOT 3
- Different transform in trainvae.py & trainmdrnn.py HOT 2
- sleep(0.1) leads to infty loops HOT 3
- inconsisent MDRNN / MDRNNCell behavoir HOT 3
- Error training MD-rnn HOT 3
- Splitting of Train and Validation / Test set HOT 1
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- Possible error when predicting next action (class RolloutGenerator) HOT 7
- a multi-process problem in the controller
- Training the controller and getting stuck in local minima HOT 5
- The definition of GMM linear layer may wrong? Or I have missed something? HOT 4
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- issue about gmm_loss HOT 2
- Controller Input HOT 1
- the train_controller always break off when trainning about 15min
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