Fence/DeepTerrainRL

terrain-adaptive locomotion skills using deep reinforcement learning

Intro

Source code for the paper: Terrain-Adaptive Locomotion Skills using Deep Reinforcement Learning

Setup

This section covers some of the steps to setup and compile the code. The software depends on many libraries that need to be carefully prepared and placed for the building and linking to work properly.

Linux

1. Caffe (http://caffe.berkeleyvision.org/installation.html)
   Specific version (https://github.com/niuzhiheng/caffe.git @ 7b3e6f2341fe7374243ee0126f5cad1fa1e44e14) sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
   sudo apt-get install --no-install-recommends libboost-all-dev
   sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
   sudo apt-get install libatlas-base-dev

   In the instruction to make and build Caffe uncomment the CPU only line

   # CPU-only switch (uncomment to build without GPU support).
   CPU_ONLY := 1
   

   Or if on Windows
   https://github.com/initialneil/caffe-vs2013

2. Boost

3. OpenCV

4. BulletPhysics

5. CUDA
   Package Manager Installation
   Install repository meta-data
   When using a proxy server with aptitude, ensure that wget is set up to use the same proxy settings before installing the cuda-repo package.
   $ sudo dpkg -i cuda-repo-.deb
   Update the Apt repository cache
   $ sudo apt-get update
   Install CUDA
   $ sudo apt-get install cuda

6. Json_cpp (https://github.com/open-source-parsers/jsoncpp)

7. Eigen (http://eigen.tuxfamily.org/index.php?title=Main_Page)

8. bits
   sudo apt-get install gcc-4.9-multilib g++-4.9-multilib

Windows

Runing The System

After the system has been build there are two executable files that server different purposes. The TerrainRL program is for visually simulating the a controller and TerrainRL_Optimize is for optimizing the parameters of some controller.

Examples:
To simulate a controller/character
./TerrainRL -arg_file= args/sim_dog_args.txt
To simulate a controller/character with a specific policy
./TerrainRL_Optimizer -arg_file= args/dog_slopes_mixed_args.txt To Train a controller
./TerrainRL_Optimizer -arg_file= args/opt_args_train_mace.txt

Key Bindings

Most of these are togglesg

• c fixed camera mode

• y draw COM path and contact locations

• q draw "filmstrip" like rendering

• f draw torques

• h draw Actor value functions and feature visualization

• shift + '>' step one frame

• p toggle draw value function

• ',' and '.' change render speed, decrease and increase.

• "spacebar" to pause simulation

• r restart the scenario

• l reload the simulation (reparses the arg file)

• g draw state features

• x spawn projectile

• z spawn big projectile

• click on character and drag to apply force