README.md

Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning

Requirements

pytorch==1.6.0, mujoco-py==2.0.2.13. All the requirements are specified in requirements.txt.

Code Usage

We demonstrate with Half-Cheetah-Vel environment. For other environments, change the argument --env-type according to the table:

Environment	Argument
Point-Robot	point_robot_v1
Half-Cheetah-Vel	cheetah_vel
Ant-Dir	ant_dir
Hopper-Param	hopper_param
Walker-Param	walker_param

Data Collection

Copy the following code into a shell script, and run the script.

for seed in {1..40}
do
	python train_data_collection.py --env-type cheetah_vel --save-models 1 --log-tensorboard 1 --seed $seed
done

Train the Task Encoder

If use generative modeling, run python train_generative_model.py --env-type cheetah_vel to pre-train the CVAE. Run python train_contrastive.py --env-type cheetah_vel --relabel-type generative --generative-model-path logs/*** --output-file-prefix contrastive_generative to train the encoder. Specify --generative-model-path with the path of the last saved CVAE model. If use reward randomization, specify --relabel-type with reward_randomize.

Offline Meta-RL

Specify --encoder-model-path with the last saved encoder, then run: python train_offpolicy_with_trained_encoder.py --env-type cheetah_vel --encoder-model-path logs/*** --output-file-prefix offpolicy_contrastive_generative. Check for the training result using Tensorboard.

OOD Test

Replace the content in the file ood_test_config/cheetah_vel.txt with paths of sampled behavior policies. Modify line 343~346 of test_ood_context.py to set the correct test model path. Then run python test_ood_context.py --env-type cheetah_vel.

Citation

If you are using the codes, please cite our paper.

@inproceedings{yuan2022robust,
    	title={Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning},
		author={Yuan, Haoqi and Lu, Zongqing},
		booktitle={International Conference on Machine Learning},
		pages={25747--25759},
		year={2022},
		organization={PMLR}
}