#!/bin/bash
# Script for running icml gridworld experiments with VIN -res networks
# 8x8 map
THEANO_FLAGS='floatX=float32,device=cpu' python NN_run_training.py --input ./data/gridworld_8.mat --output ./nips16results/gridworld/grid8_VIN_untied.pk --epochs 30 --model valIterBatchUntied --stepsize 0.01 --imsize 8 --reg 0.0 --k 10 --batchsize 12 --statebatchsize 10 | tee -a ./nips16results/gridworld/out_grid8_VIN_untied.txt ; 
THEANO_FLAGS='floatX=float32,device=cpu' python NN_run_training.py --input ./data/gridworld_8.mat --output ./nips16results/gridworld/grid8_VIN_untied.pk --epochs 30 --model valIterBatchUntied --stepsize 0.005 --imsize 8 --reg 0.0 --k 10 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid8_VIN_untied.pk | tee -a ./nips16results/gridworld/out_grid8_VIN_untied.txt ; 
THEANO_FLAGS='floatX=float32,device=cpu' python NN_run_training.py --input ./data/gridworld_8.mat --output ./nips16results/gridworld/grid8_VIN_untied.pk --epochs 30 --model valIterBatchUntied --stepsize 0.002 --imsize 8 --reg 0.0 --k 10 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid8_VIN_untied.pk | tee -a ./nips16results/gridworld/out_grid8_VIN_untied.txt ; 
THEANO_FLAGS='floatX=float32,device=cpu' python NN_run_training.py --input ./data/gridworld_8.mat --output ./nips16results/gridworld/grid8_VIN_untied.pk --epochs 30 --model valIterBatchUntied --stepsize 0.001 --imsize 8 --reg 0.0 --k 10 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid8_VIN_untied.pk | tee -a ./nips16results/gridworld/out_grid8_VIN_untied.txt ;
# 16x16 map
THEANO_FLAGS='floatX=float32,device=cpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_untied.pk --epochs 30 --model valIterBatchUntied --stepsize 0.01 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 | tee -a ./nips16results/gridworld/out_grid16_VIN_untied.txt ; 
THEANO_FLAGS='floatX=float32,device=cpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_untied.pk --epochs 30 --model valIterBatchUntied --stepsize 0.005 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_untied.pk | tee -a ./nips16results/gridworld/out_grid16_VIN_untied.txt ; 
THEANO_FLAGS='floatX=float32,device=cpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_untied.pk --epochs 30 --model valIterBatchUntied --stepsize 0.002 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_untied.pk | tee -a ./nips16results/gridworld/out_grid16_VIN_untied.txt ; 
THEANO_FLAGS='floatX=float32,device=cpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_untied.pk --epochs 30 --model valIterBatchUntied --stepsize 0.001 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_untied.pk | tee -a ./nips16results/gridworld/out_grid16_VIN_untied.txt ;
# 28x28 map
THEANO_FLAGS='floatX=float32,device=cpu' python NN_run_training.py --input ./data/gridworld_28.mat --output ./nips16results/gridworld/grid28_VIN_untied.pk --epochs 30 --model valIterBatchUntied --stepsize 0.01 --imsize 28 --reg 0.0 --k 36 --batchsize 12 --statebatchsize 10 | tee -a ./nips16results/gridworld/out_grid28_VIN_untied.txt ; 
THEANO_FLAGS='floatX=float32,device=cpu' python NN_run_training.py --input ./data/gridworld_28.mat --output ./nips16results/gridworld/grid28_VIN_untied.pk --epochs 30 --model valIterBatchUntied --stepsize 0.005 --imsize 28 --reg 0.0 --k 36 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid28_VIN_untied.pk | tee -a ./nips16results/gridworld/out_grid28_VIN_untied.txt ; 
THEANO_FLAGS='floatX=float32,device=cpu' python NN_run_training.py --input ./data/gridworld_28.mat --output ./nips16results/gridworld/grid28_VIN_untied.pk --epochs 30 --model valIterBatchUntied --stepsize 0.002 --imsize 28 --reg 0.0 --k 36 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid28_VIN_untied.pk | tee -a ./nips16results/gridworld/out_grid28_VIN_untied.txt ; 
THEANO_FLAGS='floatX=float32,device=cpu' python NN_run_training.py --input ./data/gridworld_28.mat --output ./nips16results/gridworld/grid28_VIN_untied.pk --epochs 30 --model valIterBatchUntied --stepsize 0.001 --imsize 28 --reg 0.0 --k 36 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid28_VIN_untied.pk | tee -a ./nips16results/gridworld/out_grid28_VIN_untied.txt ;

