Fix internal links

Author: PeterBowman

docs/install-anaconda.md

@@ -16,7 +16,7 @@ This usually not so easy, some package need additional steps:
 
 ## Install YARP python bindings inside anaconda
 
-Make sure you have performed a [normal installation](https://github.com/roboticslab-uc3m/installation-guides/blob/master/install-yarp.md) of YARP first. Then:
+Make sure you have performed a [normal installation](./install-yarp.md) of YARP first. Then:
 
 ```bash
 export PATH="~/anaconda2/bin:$PATH" #Activate anaconda

docs/install-nvidia-drivers.md

@@ -12,7 +12,7 @@ sudo apt update
 
 - `304.135` on GT200 GeForce GTX 260 (rev a1)
 - `384.111` on GF114M GeForce GTX 675M (rev a1)
-- `384.130` on GM200 GeForce GTX TITAN X (rev a1) (more modern also work but this one goes with CUDA, see [install-tensorflow-with-gpu-ubuntu-1604.md#working-setups](https://github.com/roboticslab-uc3m/installation-guides/blob/master/install-tensorflow.md#working-setups))
+- `384.130` on GM200 GeForce GTX TITAN X (rev a1) (more modern also work but this one goes with CUDA, see [install-tensorflow-with-gpu-ubuntu-1604.md#working-setups](./install-tensorflow.md#working-setups))
 
 ## More CUDA related (may end up in new page)
 

docs/install-softgym.md

@@ -31,7 +31,7 @@ A Dockerfile and pre-built Docker container for compiling SoftGym exists. Part o
 
 - Install [docker-ce](https://docs.docker.com/install/linux/docker-ce/ubuntu/)
 - Install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker#quickstart)
-- Install [Anaconda](https://github.com/roboticslab-uc3m/installation-guides/blob/master/install-anaconda.md)
+- Install [Anaconda](./install-anaconda.md)
 - Install Pybind11 using `conda install pybind11`
 
 ### Running pre-built Dockerfile

docs/install-tensorflow.md

@@ -36,7 +36,7 @@ Note: better than `LD_LIBRARY_PATH`, put in correct place and run `ldconfig`.
 
 ### GM200 GeForce GTX TITAN X rev a1
 
-- CUDA 9.0 (uninstalls any NVIDIA driver, installs `384.130` driver, so you may not need to [Install NVIDIA drivers](https://github.com/roboticslab-uc3m/installation-guides/blob/master/install-nvidia-drivers.md)). We go to legacy and get `deb (local)` (`cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64.deb` and patches from [here](https://developer.nvidia.com/cuda-90-download-archive?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1604&target_type=deblocal)). Official documentation is currently at CUDA 9.0, and [here](https://github.com/tensorflow/tensorflow/issues/16886#issuecomment-365108781) they say 9.1 will be skipped. + cuDNN v7.1.3 (April 17, 2018) for CUDA 9.0.
+- CUDA 9.0 (uninstalls any NVIDIA driver, installs `384.130` driver, so you may not need to [Install NVIDIA drivers](./install-nvidia-drivers.md)). We go to legacy and get `deb (local)` (`cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64.deb` and patches from [here](https://developer.nvidia.com/cuda-90-download-archive?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1604&target_type=deblocal)). Official documentation is currently at CUDA 9.0, and [here](https://github.com/tensorflow/tensorflow/issues/16886#issuecomment-365108781) they say 9.1 will be skipped. + cuDNN v7.1.3 (April 17, 2018) for CUDA 9.0.
     - tensorflow 1.8-rc0 at [e1e5f305e5359fd50340ea76ea2f737f6e87a0d7](https://github.com/tensorflow/tensorflow/tree/e1e5f305e5359fd50340ea76ea2f737f6e87a0d7) (tried 1.7 but was broken for GPU). From source, with CUDA, said Yes to cuDNN 7.0 even with 7.1.3, without TensorRT (Ubuntu 16.04 local deb v3 was installed, but said default No).
     - tensorflow 1.5 (directly using `tensorflow-gpu` binary), without TensorRT.
 - Not tested: CUDA 8.0 + tensorflow 1.4 (directly using `tensorflow-gpu` binary)
@@ -48,4 +48,3 @@ TensorFlow for GPU at https://www.tensorflow.org/install/install_linux says:
    - CUDA 9.0: http://docs.nvidia.com/cuda/cuda-installation-guide-linux/#axzz4VZnqTJ2A
        - Which recommends Drivers [390](http://www.nvidia.com/Download/driverResults.aspx/132530/en-us), with no GeForce 200 Series support (min GeForce 400 Series), but should support GeForce GTX 675M.
    - CUDA [micro-arch](https://en.wikipedia.org/wiki/CUDA#GPUs_supported) 3.0 Kepler from source, or 3.5 Kepler for bin: GTX 260 is 1.3 Tesla, and GTX 675M is 2.1 Fermi.
-