Meta Reinforcement Learning

This repository contains the TensorFlow implementation of our paper Meta Reinforcement Learning with Task Embedding and Shared Policy (IJCAI 2019).

For detailed settings of the environments and experiments, please refer to the supplementary material.

We also re-implement the following methods:

• MAML for RL
  • Agent Class
  • paper
• Meta-SGD for RL
  • Agent Class
  • paper
• MAESN
  • Agent Class
  • paper
  • Note that here we use a latent variable instead of a latent distribution.

Requirements

• python==3.6.5
• tensorflow>=1.11,<2.0
• ray>=0.6 (we used this version in the experiments, but any version ray>=0.6 should work)
• gym>=0.10
• mujoco-py==1.50.1.68

We recommend installing Anaconda before installing other dependencies.

Usage

Preparation

We provide several samples of bash scripts to ease operations on a Ray cluster:

• env.sh: Declare the configuration of Ray cluster.
• update_ray_codes.sh: Replace with our slightly modified RLlib and Tune.
• exec_commands.sh: Make some directories and (optionally) install ray on each node.
• ray_start.sh: Start a Ray cluster.
• ray_stop.sh: Stop a Ray cluster.
• upload_codes.sh: Upload the training code to each node of a Ray cluster.

Run

Distributed Mode

You need to first launch a Ray cluster declared in env.sh and upload relevant codes to each node.

Then, log into the master (head) node, switch to the working directory (see upload_codes.sh), and type

python main_train.py --env wheeled --alg tesp

Local Mode

You need to uncomment ray.init() in main_train.py.

Cite

Please cite our paper if you use this code in your own work:

@inproceedings{lan2019metarl,
  title={Meta Reinforcement Learning with Task Embedding and Shared Policy},
  author={Lan, Lin and Li, Zhenguo and Guan, Xiaohong and Wang, Pinghui},
  booktitle={IJCAI},
  year={2019}
}