Training data from Reinforced SciNet

Summary: The results from the training of neural networks in v2 of Reinforced SciNet, published partially in v2 of the paper Operationally meaningful representations of physical systems in neural networks. File description: results.txt - The results from the training during reinforcement learning. results_loss.txt - The loss from the training during representation learning. selection.txt - The noise level of latent neurons during representation learning. Parameters: Reinforcement Learning Server parameters 21 workers, 2 predictors, 1 trainer each 3M episodes Training parameters glow: 0.1 gamma: 0.01 softmax: 0.5 learning rate: 0.00005 reward clipping: 1.0e-7 Network parameters DPS model: {'env1': [128, 128, 128, 128, 64, 32], 'env2': [128, 128, 128, 128, 64, 32], 'env3': [128, 128, 128, 128, 64, 32]} Parameters: Representation Learning Server parameters 21 workers, 2 predictors, 1 trainer each 5M episodes Training parameters learning rate: 0.0001 reward clipping: 1.0e-7 selection discount: 0.04 minimization discount: 0.02 ae discount: 10.0 agent discount: 1. reward rescaling: 10 predicted actions: 1 training data: 200K Network parameters Prediction model: {'env1': [64, 128, 128, 128, 128, 64, 32], 'env2': [64, 128, 128, 128, 128, 64, 32], 'env3': [64, 128, 128, 128, 128, 64, 32]} Encoder model: [128, 128, 64, 32] Decoder model: [32, 64, 128, 128, 128]