hengyuan-hu / ibrl

Imitation Bootstrapped Reinforcement Learning

Implementation of Imitation Bootstrapped Reinforcement Learning (IBRL) and baeslines (RLPD, RFT) on Robomimic and Meta-World Tasks.

Clone and compile

Clone the repo.

We need --recursive to get the correct submodule

git clone --recursive https://github.com/hengyuan-hu/ibrl.git

Install dependencies

First Install MuJoCo

Download the MuJoCo version 2.1 binaries for Linux

Extract the downloaded mujoco210 directory into ~/.mujoco/mujoco210.

Create conda env

First create a conda env with name ibrl.

conda create --name ibrl python=3.9

Then, source set_env.sh to activate ibrl conda env. It also setup several important paths such as MUJOCO_PY_MUJOCO_PATH and add current project folder to PYTHONPATH. Note that if the conda env has a different name, you will need to manually modify the set_env.sh. You also need to modify the set_env.sh if the mujoco is not installed at the default location.

# NOTE: run this once per shell before running any script from this repo
source set_env.sh

Then install python dependencies

# first install pytorch with correct cuda version, in our case we use torch 2.1 with cu121
pip install torch==2.1.0 torchvision==0.16.0 --index-url https://download.pytorch.org/whl/cu121

# then install extra dependencies from requirement.txt
pip install -r requirements.txt

If the command above does not work for your versions. Please check out tools/core_packages.txt for a list of commands to manually install relavent packages.

Compile C++ code

We have a C++ module in the common utils that requires compilation

cd common_utils
make

Trouble Shooting

Later when running the training commands, if we encounter the following error

ImportError: .../libstdc++.so.6: version `GLIBCXX_3.4.30' not found

Then we can force the conda to use the system c++ lib. Use these command to symlink the system c++ lib into conda env. To find PATH_TO_CONDA_ENV, run echo ${CONDA_PREFIX:-"$(dirname $(which conda))/../"}.

ln -sf /lib/x86_64-linux-gnu/libstdc++.so.6 PATH_TO_CONDA_ENV/bin/../lib/libstdc++.so
ln -sf /lib/x86_64-linux-gnu/libstdc++.so.6 PATH_TO_CONDA_ENV/bin/../lib/libstdc++.so.6

Reproduce Results

Remember to run source set_env.sh once per shell before running any script from this repo.

Download data and BC models

Download dataset and models from Google Drive and put the folders under release folder. The release folder should contain release/cfgs (already shipped with the repo), release/data and release/model (the latter two are from the downloaded zip file).

Robomimic (pixel)

Train RL policy using the BC policy provided in release folder

IBRL

# can
python train_rl.py --config_path release/cfgs/robomimic_rl/can_ibrl.yaml

# square
python train_rl.py --config_path release/cfgs/robomimic_rl/square_ibrl.yaml

Use --save_dir PATH to specify where to store the logs and models. Use --use_wb 0 to disable logging to weight and bias.

Use the following commands to train a BC policy from scratch. We find that IBRL is not sensitive to the exact performance of the BC policy.

# can
python train_bc.py --config_path release/cfgs/robomimic_bc/can.yaml

# square
python train_bc.py --config_path release/cfgs/robomimic_bc/square.yaml

RLPD

# can
python train_rl.py --config_path release/cfgs/robomimic_rl/can_rlpd.yaml

# square
python train_rl.py --config_path release/cfgs/robomimic_rl/square_rlpd.yaml

RFT (Regularized Fine-Tuning)

These commands run RFT from pretrained models in release folder.

# can rft
python train_rl.py --config_path release/cfgs/robomimic_rl/can_rft.yaml

# square rft
python train_rl.py --config_path release/cfgs/robomimic_rl/square_rft.yaml

To only perform pretraining:

# can, pretraining for 5 x 10,000 steps
python train_rl.py --config_path release/cfgs/robomimic_rl/can_rft.yaml --pretrain_only 1 --pretrain_num_epoch 5 --load_pretrained_agent None

# square, pretraining for 10 x 10,000 steps
python train_rl.py --config_path release/cfgs/robomimic_rl/square_rft.yaml --pretrain_only 1 --pretrain_num_epoch 10 --load_pretrained_agent None

---

Robomimic (state)

IBRL

Train IBRL using the provided state BC policies:

# can state
python train_rl.py --config_path release/cfgs/robomimic_rl/can_state_ibrl.yaml

# square state
python train_rl.py --config_path release/cfgs/robomimic_rl/square_state_ibrl.yaml

To train a state BC policy from scratch:

# can
python train_bc.py --config_path release/cfgs/robomimic_bc/can_state.yaml

# square
python train_bc.py --config_path release/cfgs/robomimic_bc/square_state.yaml

RLPD

# can state
python train_rl.py --config_path release/cfgs/robomimic_rl/can_state_rlpd.yaml

# square state
python train_rl.py --config_path release/cfgs/robomimic_rl/square_state_rlpd.yaml

RFT

Since state policies are fast to train, we can just run pretrain and RL fine-tuning in one step.

# can
python train_rl.py --config_path release/cfgs/robomimic_rl/can_state_rft.yaml

# square
python train_rl.py --config_path release/cfgs/robomimic_rl/square_state_rft.yaml

---

Metaworld

IBRL

Train RL policy using the BC policy provided in release folder

# assembly
python mw_main/train_rl_mw.py --config_path release/cfgs/metaworld/ibrl_basic.yaml --bc_policy assembly

# boxclose
python mw_main/train_rl_mw.py --config_path release/cfgs/metaworld/ibrl_basic.yaml --bc_policy boxclose

# coffeepush
python mw_main/train_rl_mw.py --config_path release/cfgs/metaworld/ibrl_basic.yaml --bc_policy coffeepush

# stickpull
python mw_main/train_rl_mw.py --config_path release/cfgs/metaworld/ibrl_basic.yaml --bc_policy stickpull

If you want to train BC policy from scratch

python mw_main/train_bc_mw.py --dataset.path Assembly --save_dir SAVE_DIR

RPLD

Note that we still specify bc_policy to specify the task name, but we don't use it in baselines. This is special to train_rl_mw.py.

python mw_main/train_rl_mw.py --config_path release/cfgs/metaworld/rlpd.yaml --bc_policy assembly --use_wb 0

RFT

For simplicity, here this one command performs both pretraining and RL training.

python mw_main/train_rl_mw.py --config_path release/cfgs/metaworld/rft.yaml --bc_policy assembly --use_wb 0

---

Citation

@misc{hu2023imitation,
    title={Imitation Bootstrapped Reinforcement Learning},
    author={Hengyuan Hu and Suvir Mirchandani and Dorsa Sadigh},
    year={2023},
    eprint={2311.02198},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}