[Project website] [Paper] [arXiv]

This project is a PyTorch implementation of Skill-based Model-based Reinforcement Learning, published in CoRL 2022.

• run.py: launches an appropriate trainer based on algorithm
• skill_trainer.py: trainer for skill-based approaches
• skimo_agent.py: model and training code for SkiMo
• skimo_rollout.py: rollout with SkiMo agent
• spirl_tdmpc_agent.py: model and training code for SPiRL+TD-MPC
• spirl_tdmpc_rollout.py: rollout with SPiRL+TD-MPC
• spirl_dreamer_agent.py: model and training code for SPiRL+Dreamer
• spirl_dreamer_rollout.py: rollout with SPiRL+Dreamer
• spirl_trainer.py: trainer for SPiRL
• spirl_agent.py: model for SPiRL
• config/: default hyperparameters
• calvin/: CALVIN environments
• d4rl/: D4RL environments forked by Karl Pertsch. The only change from us is in the installation command
• envs/: environment wrappers
• spirl/: SPiRL code
• data/: offline data directory
• rolf/: implementation of RL algorithms from robot-learning by Youngwoon Lee
• log/: training log, evaluation results, checkpoints

• Ubuntu 20.04
• Python 3.9
• MuJoCo 2.1

1. Clone this repository.

git clone --recursive [email protected]:clvrai/skimo.git
cd skimo

2. Create a virtual environment

conda create -n skimo_venv python=3.9
conda activate skimo_venv

3. Install MuJoCo 2.1

• Download the MuJoCo version 2.1 binaries for Linux or OSX.
• Extract the downloaded mujoco210 directory into ~/.mujoco/mujoco210.

4. Install packages

sh install.sh

# Navigate to the data directory
mkdir data && cd data

# Maze
gdown 1GWo8Vr8Xqj7CfJs7TaDsUA6ELno4grKJ

# Kitchen (and mis-aligned kitchen)
gdown 1Fym9prOt5Cu_I73F20cdd3lXZPhrvEsd

# CALVIN
gdown 1g4ONf_3cNQtrZAo2uFa_t5MOopSr2DNY

cd ..

Commands for SkiMo and all baselines. Results will be logged to WandB. Before running the commands below, please change the wandb entity in run.py#L36 to match your account.

Please replace [ENV] with one of maze, kitchen, calvin. For mis-aligned kitchen, append env.task=misaligned to the downstream RL command. After pre-training, please set [PRETRAINED_CKPT] with the proper path to the checkpoint.

• Pre-training

python run.py --config-name skimo_[ENV] run_prefix=test gpu=0 wandb=true

You can also skip this step by downloading our pre-trained model checkpoints. See instructions in pretrained_models.md.

• Downstream RL

python run.py --config-name skimo_[ENV] run_prefix=test gpu=0 wandb=true rolf.phase=rl rolf.pretrain_ckpt_path=[PRETRAINED_CKPT]

python run.py --config-name dreamer_config env=[ENV] run_prefix=test gpu=0 wandb=true

python run.py --config-name tdmpc_config env=[ENV] run_prefix=test gpu=0 wandb=true

• Need to first pre-train or download the skill prior (see instructions here).
• Downstream RL

python run.py --config-name spirl_config env=[ENV] run_prefix=test gpu=0 wandb=true

• Downstream RL

python run.py --config-name spirl_dreamer_[ENV] run_prefix=test gpu=0 wandb=true

• Downstream RL

python run.py --config-name spirl_tdmpc_[ENV] run_prefix=test gpu=0 wandb=true

• Downstream RL

python run.py --config-name skimo_[ENV] run_prefix=sac gpu=0 wandb=true rolf.phase=rl rolf.use_cem=false rolf.n_skill=1 rolf.prior_reg_critic=true rolf.sac=true rolf.pretrain_ckpt_path=[PRETRAINED_CKPT]

• Pre-training

python run.py --config-name skimo_[ENV] run_prefix=no_joint gpu=0 wandb=true rolf.joint_training=false

• Downstream RL

python run.py --config-name skimo_[ENV] run_prefix=no_joint gpu=0 wandb=true rolf.joint_training=false rolf.phase=rl rolf.pretrain_ckpt_path=[PRETRAINED_CKPT]

Solution: install mpi4py with conda instead, which requires a lower version of python.

conda install python==3.8
conda install mpi4py

Now you can re-run sh install.sh.

See this. In my case, I needed to change /usr/local/ to /opt/homebrew/ in all paths.

If you find our code useful for your research, please cite:

@inproceedings{shi2022skimo,
  title={Skill-based Model-based Reinforcement Learning},
  author={Lucy Xiaoyang Shi and Joseph J. Lim and Youngwoon Lee},
  booktitle={Conference on Robot Learning},
  year={2022}
}

• This code is based on Youngwoon's robot-learning repo: https://github.com/youngwoon/robot-learning
• SPiRL: https://github.com/clvrai/spirl
• TD-MPC: https://github.com/nicklashansen/tdmpc
• Dreamer: https://github.com/danijar/dreamer
• D4RL: https://github.com/rail-berkeley/d4rl
• CALVIN: https://github.com/mees/calvin