dmksjfl / MCQ

Code for Mildly Conservative Q-learning for Offline Reinforcement Learning (NeurIPS 2022)

Mildly Conservative $Q$-learning (MCQ) for Offline Reinforcement Learning

Original PyTorch implementation of MCQ (NeurIPS 2022) from Mildly Conservative Q-learning for Offline Reinforcement Learning. The code is highly based on the offlineRL repository.

Install

To use this codebase, one need to install the following dependencies:

• fire
• loguru
• tianshou==0.4.2
• gym<=0.18.3
• mujoco-py==2.0.2.8
• sklearn
• gtimer
• torch==1.8.0
• d4rl==1.1
• rlkit==0.2.1dev
• neorl==0.3.0 (https://github.com/polixir/NeoRL)

Once you have all the dependencies installed, run the following command

pip install -e .

I use python=3.8.5 to run all of the experiments. If you encounter errors of python version conflict, you can try run MCQ in python3.8 environment.

How to run

For MuJoCo tasks, we conduct experiments on d4rl MuJoCo "-v2" datasets by calling

python examples/train_d4rl.py --algo_name=MCQ --task d4rl-hopper-medium-replay-v2 --seed 6 --lam 0.9 --log-dir=logs/hopper-medium-replay/r6

For Adroit "-v0"/maze2d "-v1" tasks, we run on these datasets by calling

python examples/train_d4rl.py --algo_name=MCQ --task d4rl-maze2d-medium-v1 --seed 6 --lam 0.9 --log-dir=logs/maze2d-medium-v1/r6

The log is stored in the --log-dir. One can see the training curve via tensorboard.

To modify the number of sampled actions, specify --num tag, default is 10. To add normalization to offline data, specify --normalize tag (but this is not required).

Instruction

In the paper and our implementation, we update the critics via: $\mathcal{L}_{critic} = \lambda \mathbb{E}_{s,a,s^\prime\sim\mathcal{D},a^\prime\sim\pi(\cdot|s^\prime)}[(Q(s,a) - y)^2] + (1-\lambda)\mathbb{E}_{s\sim\mathcal{D},a\sim\pi(\cdot|s)}[(Q(s,a) - y^\prime)^2]$. While one can also try to update the critic via: $\mathcal{L}_{critic} = \mathbb{E}_{s,a,s^\prime\sim\mathcal{D},a^\prime\sim\pi(\cdot|s^\prime)}[(Q(s,a) - y)^2] + \alpha\mathbb{E}_{s\sim\mathcal{D},a\sim\pi(\cdot|s)}[(Q(s,a) - y^\prime)^2]$. It is also reasonable since we ought not to let $\lambda=0$. At this time, $\alpha = \frac{1-\lambda}{\lambda}, \lambda\in(0,1)$. Note that the hyperparameter scale would vastly change using $\alpha$ (e.g., if we let $\lambda = 0.1, \alpha=9$ while if $\lambda=0.5, \alpha=1$).

We do welcome the reader to try running with $\alpha$-style update rule.

Citation

If you use our method or code in your research, please consider citing the paper as follows:

@inproceedings{lyu2022mildly,
 title={Mildly Conservative Q-learning for Offline Reinforcement Learning},
 author={Jiafei Lyu and Xiaoteng Ma and Xiu Li and Zongqing Lu},
 booktitle={Thirty-sixth Conference on Neural Information Processing Systems},
 year={2022}
}