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

Last update: Dec 27, 2022

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

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

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

Related tags

Overview

Robot Action Primitives (RAPS)

[Project Website]

Requirements

Training and Evaluation

Kitchen

Metaworld

Robosuite

Learning Curve visualization

Owner

Murtaza Dalal

Pytorch implementation of Bert and Pals: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning

clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging

3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Normal Face Photos

A complete, self-contained example for training ImageNet at state-of-the-art speed with FFCV

The pure and clear PyTorch Distributed Training Framework.

Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

Checkout some cool self-projects you can try your hands on to curb your boredom this December!

Wider-Yolo Kütüphanesi ile Yüz Tespit Uygulamanı Yap

code for paper"A High-precision Semantic Segmentation Method Combining Adversarial Learning and Attention Mechanism"

Implementation of temporal pooling methods studied in [ICIP'20] A Comparative Evaluation Of Temporal Pooling Methods For Blind Video Quality Assessment

keyframes-CNN-RNN(action recognition)

Generalized Data Weighting via Class-level Gradient Manipulation

OpenLT: An open-source project for long-tail classification

MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks.

CUP-DNN is a deep neural network model used to predict tissues of origin for cancers of unknown of primary.

BT-Unet: A-Self-supervised-learning-framework-for-biomedical-image-segmentation-using-Barlow-Twins

U-Net: Convolutional Networks for Biomedical Image Segmentation

[WACV 2022] Contextual Gradient Scaling for Few-Shot Learning

🔊 Audio and fastai v2