SSubhnil / RIA_base

RIA base version. With new Walker environment similar to DM Control Suite physics and reward function.

A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning

TensorFlow implementation of "A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning" (ICLR 2022).

Method

An overview of our Relational Intervention approach, where Relational Encoder, Prediction Head and Relational Head are three learnable functions. Specifically, prediction Loss enables the estimated environmental-specified factor can help the Prediction head to predict the next states, and the relation Loss aims to enforce the similarity between factors estimated from the same trajectory or environments.

Because the mediator in other paths e.g. $S_t$, $A_t$, may amplify or reduce the causal effect of environmental-specific $Z$, we only consider the direct path from $Z$ to the next state(denote by the red line at Figure \ref{fig:inter} (a)), which means that we need to block all paths with meditors from $\hat{{Z}}$ to $S_{t+1}$.

• Paper

Instruction

Install MuJoCo 2.1.0 at ~/.mujoco/mujoco210 and copy your license key to ~/.mujoco/mjkey.txt

Install required packages with below commandsv:

conda create -n ria python=3.6
pip install -r requirements.txt
conda activate ria

Train and evaluate agents:

python -m run_scripts.run_ria --dataset [pendulum/hopper/slim_humanoid/halfcheetah/cripple_halfcheetah] --normalize_flag  --relation_flag 1 --contrast_flag 1 

Reference

@article{guo2022relational,
  title={A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning},
  author={Guo, Jixian and Gong, Mingming and Tao, Dacheng},
  journal={arXiv preprint arXiv:2206.04551},
  year={2022}
}

Note: this code is based on the previous work by Kimin Lee and Younggyo Seo. Many thanks to Kimin Lee and Younggyo Seo.