This repository is the official implementation of Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives (RAPS).
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}
}
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>
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>
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>
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 <>
cd /path/to/raps/rlkit
python ../viskit/viskit/frontend.py data/<exp_prefix> //open localhost:5000 to view
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