[NeurIPS 2021] PyTorch Code for Accelerating Robotic Reinforcement Learning with Parameterized Action Primitives

Related tags

Deep Learningreinforcement-learningdeep-learningroboticsartificial-intelligenceprimitives
Overview

Robot Action Primitives (RAPS)

This repository is the official implementation of Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives (RAPS).

[Project Website]

Murtaza Dalal, Deepak Pathak*, Ruslan Salakhutdinov*
(* equal advising)

CMU

alt text

If you find this work useful in your research, please cite:

@inproceedings{dalal2021raps,
    Author = {Dalal, Murtaza and Pathak, Deepak and
              Salakhutdinov, Ruslan},
    Title = {Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives},
    Booktitle = {NeurIPS},
    Year = {2021}
}

Requirements

To install dependencies, please run the following commands:

sudo apt-get update
sudo apt-get install curl \
    git \
    libgl1-mesa-dev \
    libgl1-mesa-glx \
    libglew-dev \
    libosmesa6-dev \
    software-properties-common \
    net-tools \
    unzip \
    vim \
    virtualenv \
    wget \
    xpra \
    xserver-xorg-dev
sudo apt-get install libglfw3-dev libgles2-mesa-dev patchelf
sudo mkdir /usr/lib/nvidia-000

Please add the following to your bashrc:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mujoco200/bin
export MUJOCO_GL='egl'
export MKL_THREADING_LAYER=GNU
export D4RL_SUPPRESS_IMPORT_ERROR='1'
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia-000

To install python requirements:

conda create -n raps python=3.7
conda activate raps
./setup_python_env.sh <absolute path to raps>

Training and Evaluation

Kitchen

Prior to running any experiments, make sure to run cd /path/to/raps/rlkit

single task env names:

  • microwave
  • kettle
  • slide_cabinet
  • hinge_cabinet
  • light_switch
  • top_left_burner

multi task env names:

  • microwave_kettle_light_top_left_burner //Sequential Multi Task 1
  • hinge_slide_bottom_left_burner_light //Sequential Multi Task 2

To train RAPS with Dreamer on any single task kitchen environment, run:

python experiments/kitchen/dreamer/dreamer_v2_single_task_primitives.py --mode here_no_doodad --exp_prefix <> --env <env name>

To train RAPS with Dreamer on the multi task kitchen environments, run:

python experiments/kitchen/dreamer/dreamer_v2_multi_task_primitives.py --mode here_no_doodad --exp_prefix <> --env <env name>

To train Raw Actions with Dreamer on any kitchen environment

python experiments/kitchen/dreamer/dreamer_v2_raw_actions.py --mode here_no_doodad --exp_prefix <> --env <env name>

To train RAPS with RAD on any single task kitchen environment

python experiments/kitchen/rad/rad_single_task_primitives.py --mode here_no_doodad --exp_prefix <> --env <env name>

To train RAPS with RAD on any multi task kitchen environment

python experiments/kitchen/rad/rad_multi_task_primitives.py --mode here_no_doodad --exp_prefix <> --env <env name>

To train Raw Actions with RAD on any kitchen environment

python experiments/kitchen/rad/rad_raw_actions.py --mode here_no_doodad --exp_prefix <> --env <env name>

To train RAPS with PPO on any single task kitchen environment

python experiments/kitchen/ppo/ppo_single_task_primitives.py --mode here_no_doodad --exp_prefix <> --env <env name>

To train RAPS with PPO on any multi task kitchen environment

python experiments/kitchen/ppo/ppo_multi_task_primitives.py --mode here_no_doodad --exp_prefix <> --env <env name>

To train Raw Actions with PPO on any kitchen environment

python experiments/kitchen/ppo/ppo_raw_actions.py --mode here_no_doodad --exp_prefix <> --env <env name>

Metaworld

single task env names

  • drawer-close-v2
  • soccer-v2
  • peg-unplug-side-v2
  • sweep-into-v2
  • assembly-v2
  • disassemble-v2

To train RAPS with Dreamer on any metaworld environment

python experiments/metaworld/dreamer/dreamer_v2_single_task_primitives.py --mode here_no_doodad --exp_prefix <> --env <env name>

To train Raw Actions with Dreamer on any metaworld environment

python experiments/metaworld/dreamer/dreamer_v2_single_task_raw_actions.py --mode here_no_doodad --exp_prefix <> --env <env name>

Robosuite

To train RAPS with Dreamer on an Robosuite Lift

python experiments/robosuite/dreamer/dreamer_v2_single_task_primitives_lift.py --mode here_no_doodad --exp_prefix <>

To train Raw Actions with Dreamer on an Robosuite Lift

python experiments/robosuite/dreamer/dreamer_v2_single_task_raw_actions_lift.py --mode here_no_doodad --exp_prefix <>

To train RAPS with Dreamer on an Robosuite Door

python experiments/robosuite/dreamer/dreamer_v2_single_task_primitives_door.py --mode here_no_doodad --exp_prefix <>

To train Raw Actions with Dreamer on an Robosuite Door

python experiments/robosuite/dreamer/dreamer_v2_single_task_raw_actions_door.py --mode here_no_doodad --exp_prefix <>

Learning Curve visualization

cd /path/to/raps/rlkit
python ../viskit/viskit/frontend.py data/<exp_prefix> //open localhost:5000 to view
Owner
Murtaza Dalal
Passionate about Machine Learning, Computer Vision, Robotics, and AI. Interested in seamlessly integrating software and hardware into into intelligent systems.
Murtaza Dalal
GitHub Repository https://mihdalal.github.io/raps/
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