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run build.sh to install requirements
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Results of both models with different DoFs are stored in src/figures/evaluation/results/
- learning rate (<= 0.01)
- batch_size
- amount of hidden layers per encoder/decoder (>= 1)
- amount of neurons per hidden layer (>= 100)
- variational beta
- (loss weightings)
- learning rate (<= 0.001)
- batch_size
- amount of coupling layers (>= 2)
- amount of hidden layers per subnetwork (>= 1)
- amount of neurons per hidden layer (>= 100)
- (loss weightings)
- implement cVAE model
- implement planar robot simulation with 2 and 3 DoF
- implement paper's robot simulation
- implement rejection sampling
- implement random search for hyperparameter optimization (https://docs.ray.io/en/master/tune/)
- implement basic INN model
- implement backward training of INN model
- debug MMD loss