Awesome

Robot Learning Framework for Research

RL algorithms

• PPO
• DDPG
• TD3
• SAC

IL algorithms

• BC
• GAIL
• DAC

Directories

• run.py: simply launches main.py
• main.py: sets up experiment and runs training using trainer.py
• trainer.py: contains training and evaluation code
• algorithms/: implementation of all RL and IL algorithms
• config/: hyper-parameters in config/__init__.py
• environments/: registers environments (OpenAI Gym and Deepmind Control Suite)
• networks/: implementation of networks, such as policy and value function
• utils/: contains helper functions

Prerequisites

• Ubuntu 18.04 or above
• Python 3.6
• Mujoco 2.0

Installation

1. Install mujoco 2.0 and add the following environment variables into ~/.bashrc or ~/.zshrc

# download mujoco 2.0
$ wget https://www.roboti.us/download/mujoco200_linux.zip -O mujoco.zip
$ unzip mujoco.zip -d ~/.mujoco
$ cp -r ~/.mujoco/mujoco200_linux ~/.mujoco/mujoco200

# copy mujoco license key `mjkey.txt` to `~/.mujoco`

# add mujoco to LD_LIBRARY_PATH
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/.mujoco/mujoco200/bin

# for GPU rendering (replace 418 with your nvidia driver version or you can make a dummy directory /usr/lib/nvidia-000)
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia-418

# only for a headless server
$ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libGLEW.so:/usr/lib/nvidia-418/libGL.so

2. Install python dependencies

$ sudo apt-get install cmake libopenmpi-dev libgl1-mesa-dev libgl1-mesa-glx libosmesa6-dev patchelf libglew-dev

# software rendering
$ sudo apt-get install libgl1-mesa-glx libosmesa6 patchelf

# window rendering
$ sudo apt-get install libglfw3 libglew2.0

$ pip install -r requirements.txt

Usage

PPO

$ python -m run --run_prefix test --algo ppo --env "Hopper-v2"

DDPG

$ python -m run --run_prefix test --algo ddpg --env "Hopper-v2"

TD3

$ python -m run --run_prefix test --algo td3 --env "Hopper-v2"

SAC

$ python -m run --run_prefix test --algo sac --env "Hopper-v2"

BC

1. Generate demo using PPO

# train ppo expert agent
$ python -m run --run_prefix test --algo ppo --env "Hopper-v2"
# collect expert trajectories using ppo expert policy
$ python -m run --run_prefix test --algo ppo --env "Hopper-v2" --is_train False --record_video False --record_demo True --num_eval 100
# 100 trajectories are stored in log/Hopper-v2.ppo.test.123/demo/Hopper-v2.ppo.test.123_step_00001000000_100.pkl

2. Run BC

$ python -m run --run_prefix test --algo bc --env "Hopper-v2" --demo_path log/Hopper-v2.ppo.test.123/demo/Hopper-v2.ppo.test.123_step_00001000000_100.pkl

GAIL

$ python -m run --run_prefix test --algo gail --env "Hopper-v2" --demo_path log/Hopper-v2.ppo.test.123/demo/Hopper-v2.ppo.test.123_step_00001000000_100.pkl

# initialize with BC policy
$ python -m run --run_prefix test --algo gail --env "Hopper-v2" --demo_path log/Hopper-v2.ppo.test.123/demo/Hopper-v2.ppo.test.123_step_00001000000_100.pkl --init_ckpt_path log/Hopper-v2.bc.test.123/ckpt_00000020.pt

To dos

• BC intialization for all algorithms
• Ray
• HER
• Skill coordination