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Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning

Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang

This repository is the official code release for paper Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning, published in ICML 2023. We provide a PyTorch implementation of the proposed Quasimetric RL algorithm (QRL).

Quasimetric RL (QRL) Objective

$%Please view this section in browser % \textsf{Learning the {\color[RGB]{230,97,0}quasimetric geometry}: {\color[RGB]{199,61,160}local} costs} \rightarrow \textsf{{\color{teal}global} optimal paths} $

$$%Please view this section in browser \underbrace{\max_{\theta}~\mathbb{E}_{\substack{s\sim p_\mathsf{state}\\g \sim p_\mathsf{goal}}}[{ \overbrace{d_\theta}^{\color[RGB]{230,97,0}\llap{\textsf{quasimetr}}\rlap{\textsf{ic model}}}}(s, g)]}_{\textsf{push apart {\color{teal}all state-goal pairs}}} \quad\quad \text{subject to}\qquad \underbrace{\mathbb{E}_{\substack{(s, a, s', \mathsf{cost}) \sim p_\mathsf{transition}}}[ \mathtt{relu}( d_\theta(s, s') - \mathsf{cost} )^2] \leq { \overbrace{ \epsilon^2 }^{\color{gray}\llap{\epsilon\textsf{ is a }}\rlap{\textsf{small positive constant}}} }}_{\textsf{not overestimate observed {\color[RGB]{199,61,160}local} distances/costs}}\tag{QRL}$$

See webpage for explanation.

Requirements

The code has been tested on

  • CUDA 11 with NVIDIA RTX Titan, NVIDIA 2080Ti, NVIDIA Titan XP, NVIDIA V100, and NVIDIA 3080.

Software dependencies (also in requirements.txt):

torch>=1.13.1
tqdm
numpy>=1.17.0
imageio==2.6.1
gym==0.18.0
attrs>=21.4.0
hydra-core==1.3.2
omegaconf==2.3.0
d4rl==1.1
mujoco==2.3.6

Note

d4rl depends on mujoco_py which can be difficult to install. The code lazily imports mujoco_py and d4rl if the user requests such environments. Therefore, their installation is not necessary to run the QRL algorithm, e.g., on a custom environment. However, running QRL on the provided environments (d4rl.maze2d and GCRL) requires them.

Code structure

  • quasimetric_rl.modules implements the actor and critic components, as well as their associated QRL losses.
  • quasimetric_rl.data implements data loading and memory buffer utilities, as well as creation of environments.
  • online.main provides an entry point to online experiments.
  • offline.main provides an entry point to offline experiments.

Online and offline settings mostly differ in the usage of data storage:

  • Offline: static dataset.
  • Online: replay buffer that dynamically grows and stores more experiences.

In both online.main and offline.main, there is a Conf object, containing all the provided knobs you can customize QRL behavior. This Conf object is updated with commandline arguments via hydra, and then used to create the modules and losses.

Examples

To reproduce the offline d4rl experiments in paper, you can use commands similar to these:

# run umaze seed=12131415 device.index=2
./offline/run_maze2d.sh env.name='maze2d-umaze-v1'

# run medium maze with custom seed, the GPU at index 2, and not training an actor
./offline/run_maze2d.sh env.name='maze2d-medium-v1' seed=12131415 device.index=2 agent.actor=null

# run large maze with custom seed, the GPU at index 3, and 100 gradient steps
./offline/run_maze2d.sh env.name='maze2d-large-v1' seed=44411223 device.index=3 total_optim_steps=100

To reproduce the online gcrl experiments in paper, you can use commands similar to these:

# run state-input FetchReach
./online/run_gcrl.sh env.name='FetchReach'

# run image-input FetchPush with custom seed and the GPU at index 2
./online/run_gcrl.sh env.name='FetchPushImage' seed=12131415 device.index=2

# run state-input FetchSlide with custom seed, 10 environment steps, and 3 critics
./online/run_gcrl.sh env.name='FetchSlide' seed=44411223 interaction.total_env_steps=10 agent.num_critics=3

[SGI] To run on your custom environment use:

./offline/run_sgi.sh env.name='custom-grid-tank-goal-v1' agent.actor=null total_optim_steps=10000 agent.num_critics=1

Before that, run an offline trajectory with:

python save_trajectory.py
Example code for how to load a trained checkpoint (click me) 
import os
import torch
from omegaconf import OmegaConf, SCMode
import yaml

from quasimetric_rl.data import Dataset
from quasimetric_rl.modules import QRLAgent, QRLConf


expr_checkpoint = '/xxx/xx/xx/xxxx.pth'  # FIXME


expr_dir = os.path.dirname(expr_checkpoint)
with open(expr_dir + '/config.yaml', 'r') as f:
    # load saved conf
    conf = OmegaConf.create(yaml.safe_load(f))


# 1. How to create env
dataset: Dataset = Dataset.Conf(kind=conf.env.kind, name=conf.env.name).make(dummy=True)  # dummy: don't load data
env = dataset.create_env()  # <-- you can use this now!
# episodes = list(dataset.load_episodes())  # if you want to load episodes for offline data


# 2. How to re-create QRL agent
agent_conf: QRLConf = OmegaConf.to_container(
  OmegaConf.merge(OmegaConf.structured(QRLConf()), conf.agent),  # overwrite with loaded conf
  structured_config_mode=SCMode.INSTANTIATE,  # create the object
)
agent: QRLAgent = agent_conf.make(env_spec=dataset.env_spec, total_optim_steps=1)[0]  # you can move to your fav device


# 3. Load checkpoint
agent.load_state_dict(torch.load(expr_checkpoint, map_location='cpu')['agent'])

Note

  1. We recommend monitoring experiments with tensorboard.
  2. [Offline Only] if you do not want to train an actor (e.g., because the action space is discrete and the code only implements policy training via backpropagating through quasimetric critics), add agent.actor=null.
  3. Environment flag QRL_DEBUG=1 will enable additional checks and automatic pdb.post_mortem. It is your debugging friend.
  4. Adding environments can be done via quasimetric_rl.data.register_(online|offline)_env. See their docstrings for details. To construct an quasimetric_rl.data.EpisodeData from a trajectory, see the EpisodeData.from_simple_trajectory helper constructor.

Citation

Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang. "Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning" International Conference on Machine Learning (ICML). 2023.

@inproceedings{tongzhouw2023qrl,
  title={Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning},
  author={Wang, Tongzhou and Torralba, Antonio and Isola, Phillip and Zhang, Amy},
  booktitle={International Conference on Machine Learning},
  organization={PMLR},
  year={2023}
}

Questions

For questions about the code provided in this repository, please open an GitHub issue.

For questions about the paper, please contact Tongzhou Wang (tongzhou AT mit DOT edu).

License

This repo is under MIT license. Please check LICENSE file.