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AdaRL

Implementation codes and datasets used in paper AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning (ICLR'22 Spotlight).

Paper: [ICLR2022], [arXiv]

Introduction

One practical challenge in reinforcement learning (RL) is how to make quick adaptations when faced with new environments. In this paper, we propose a principled framework for adaptive RL, called AdaRL, that adapts reliably and efficiently to changes across domains with a few samples from the target domain, even in partially observable environments. Specifically, we leverage a parsimonious graphical representation that characterizes structural relationships over variables in the RL system. Such graphical representations provide a compact way to encode what and where the changes across domains are, and furthermore inform us with a minimal set of changes that one has to consider for the purpose of policy adaptation. We show that by explicitly leveraging this compact representation to encode changes, we can efficiently adapt the policy to the target domain, in which only a few samples are needed and further policy optimization is avoided. We illustrate the efficacy of AdaRL through a series of experiments that vary factors in the observation, transition and reward functions for Cartpole and Atari games.

Requirements

The current version of the code has been tested with following libs:

  • cudatoolkit==10.0.130
  • tensorflow-gpu 1.15.0
  • cudatoolkit 10.0.130
  • gym 0.18.0
  • Pillow
  • keras 2.6.0
  • numpy 1.20.2
  • opencv-python 4.5.1.48

Install the required the packages inside the virtual environment:

$ conda create -n yourenvname python=3.7 anaconda
$ source activate yourenvname
$ conda install cudatoolkit==10.0.130
$ pip install -r requirements.txt

Installation for envs

Cartpole

cd libs/gym-cartpole-world-master
pip install -e .

Take gravity of 9.8 as an example.

python gym_cartpole_world_usage.py

All the version details can be found in libs/gym-cartpole-world-master/gym_cartpole_world/__init__.py.

Data preparation

Take cartpole with gravity of 5 as an example.

cd ../../
python data/data_gen_cartpoleworld.py 'v00'
Atari Pong

Install gym_pong.

cd libs/gym_pong-master
pip install -e .

Follow this instruction to import ROMs

Take size of 2.0 as an example.

python gym_pong_usage.py

All the version details can be found in libs/gym_pong-master/gym_pong/__init__.py.

Data preparation

Take the domain in size of 2.0 as an example

cd ../../
python data/data_gen_pong.py -name 'Pongm' -v v00

For the reward-varying cases

python data/data_gen_pong.py -name 'Pong' -v v0 -rewardm 'linear' -k1 0.1

python data/data_gen_pong.py -name 'Pong' -v v0 -rewardm 'non-linear' -k2 2.0

Model estimation

Take cartpole with domain across gravities as an example

python model_est.py -name cartpole -source ./dataset/Cartpole/G_len40_xthreshold5_thetathreshold45_trial10000 -dest ./dataset/Cartpole/G_len40_xthreshold5_thetathreshold45_trial10000/v_train -domain 00 01 02 03 04

P.S. instead of directly training the whole model, we divide the whole process into 4 steps, i.e., VAE, series, dynamics and then the whole model with parameters initialized by previous parts.

Policy optimization

Take cartpole with domain across gravities as an example

python policy_opt.py -name cartpole  -mvae_p ./results/Cartpole/G_len40_xthreshold5_thetathreshold45_trial10000/all/{TIME_NOW}/all.json -source ./dataset/Cartpole/G_len40_xthreshold5_thetathreshold45_trial10000 -domain 00 01 02 03 04

On testing data

python test.py -name cartpole -source ./dataset/Cartpole/G_len40_xthreshold5_thetathreshold45_trial10000 -dest ./dataset/Cartpole/G_len40_xthreshold5_thetathreshold45_trial10000/v_test -domain 05 06 -mvae_p ./results/Cartpole/G_len40_xthreshold5_thetathreshold45_trial10000/all/{TIME_NOW}/all.json -k 0 -step 1
python test.py -name cartpole -source ./dataset/Cartpole/G_len40_xthreshold5_thetathreshold45_trial10000 -dest ./dataset/Cartpole/G_len40_xthreshold5_thetathreshold45_trial10000/v_test -domain 05 06 -mvae_p ./results/Cartpole/G_len40_xthreshold5_thetathreshold45_trial10000/all/{TIME_NOW}/all.json -k 0 -step 2

Notes

The model estimation stage will cost much CUDA memory. Thus GPU devices with large memory (e.g., V100 cards) are required. Pytorch version will be released later.

Citation

If you find our work helpful to your research, please consider citing our paper:

@InProceedings{huang2021adarl,
  title={AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning},
  author={Huang, Biwei and Feng, Fan and Lu, Chaochao and Magliacane, Sara and Zhang, Kun},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2022}
}

Acknowledgements

Parts of code were built upon world model implementation by David Ha.