A Production Tool for Embodied AI.
Project description
:scroll:Loopquest
A Production Tool for Embodied AI.
- :video_camera:Quickstart Demo, Dataset Demo
- :house:Discord
Major features
- Imitation Learning / Offline Reinforcement Learning Oriented MLOps. Log all the observation, action, reward, rendered images into database with only ONE extra line of code.
env = gymnasium.make("MountainCarContinuous-v0", render_mode="rgb_array")
->
import loopquest
loopquest.init()
env = loopquest.make_env(
"MountainCarContinuous-v0", render_mode="rgb_array")
)
- You can also evaluate the local policy or the policy saved in Huggingface directly by specifying number of episodes and number of steps for each episode.
Local Policy evaluation:
import loopquest
from loopquest.eval import evaluate_local_policy
from loopquest.policy.base import BasePolicy
class RandomPolicy(BasePolicy):
def __init__(self, action_space):
self.action_space = action_space
def compute_action(self, observation):
return self.action_space.sample()
policy = RandomPolicy(env.action_space)
evaluate_local_policy(
policy,
[
"FetchPickAndPlace-v2",
"FetchPushDense-v2",
"FetchReachDense-v2",
"FetchSlideDense-v2",
],
num_episodes=1,
num_steps_per_episode=100,
project_name="test_robotics",
)
Remote Policy evaluation:
import loopquest
from loopquest.eval import evaluate_local_policy, evaluate_remote_policy
loopquest.init()
# Cloud evaluation example
evaluate_remote_policy(
"jxx123/ppo-LunarLander-v2",
"ppo-LunarLander-v2.zip",
"PPO",
["LunarLander-v2"],
num_episodes=1,
num_steps_per_episode=100,
project_name="test_lunar_remote",
experiment_configs={"foo": [1, 2, 3], "bar": "hah", "bar": 1.1},
)
- Directly trainable data for robotics foundation model. Select and download the (observation, action, reward) data with the dataloader interfaces of the most popular deep learning frameworks (e.g. tensorflow, pytorch, huggingface dataset apis). Check Dataset Quickstart Example for more details.
from loopquest.datasets import load_dataset, load_datasets
# Load data from a single experiment
ds = load_dataset("your_experiment_id")
# Load data from multiple experiments
ds = load_datasets(["exp1", "exp2"])
The data schema will look like
{
'id': '34yixvic-0-1',
'creation_time': '2023-09-03T20:53:30.603',
'update_time': '2023-09-03T20:53:30.965',
'experiment_id': '34yixvic',
'episode': 0,
'step': 1,
'observation': [-0.55, 0.00],
'action': [0.14],
'reward': -0.00,
'prev_observation': [-0.55, 0.00],
'termnated': False,
'truncated': False,
'done': False,
'info': '{}',
'sub_goal': None,
'image_ids': ['34yixvic-0-1-0'],
'images': [<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=600x400 at 0x7F8D33094450>]
}
- All the regular MLOps features are included, e.g. data visualization, simulation rendering, experiment management.
Installation
For stable version, run
pip install loopquest
For dev version or loopquest project contributors, clone the git to your local machine by running
git clone https://github.com/LoopMind-AI/loopquest.git
Change to the project root folder and install the package
cd loopquest
pip install -e .
Quick Start Examples
Run Local or Remote Eval
Run examples/run_local_eval.py.
import loopquest
from loopquest.eval import evaluate_local_policy
from loopquest.policy.base import BasePolicy
import gymnasium as gym
class RandomPolicy(BasePolicy):
def __init__(self, action_space):
self.action_space = action_space
def compute_action(self, observation):
return self.action_space.sample()
# Create this env just to get the action space.
env = gym.make("FetchPickAndPlace-v2")
policy = RandomPolicy(env.action_space)
loopquest.init()
evaluate_local_policy(
policy,
[
"FetchPickAndPlace-v2",
"FetchPushDense-v2",
"FetchReachDense-v2",
"FetchSlideDense-v2",
],
num_episodes=1,
num_steps_per_episode=100,
project_name="test_robotics_new",
)
Run examples/run_remote_eval.py.
import loopquest
from loopquest.eval import evaluate_remote_policy
loopquest.init()
evaluate_remote_policy(
"jxx123/ppo-LunarLander-v2",
"ppo-LunarLander-v2.zip",
"PPO",
["LunarLander-v2"],
num_episodes=1,
num_steps_per_episode=100,
project_name="test_lunar_remote",
experiment_configs={"foo": [1, 2, 3], "bar": "hah", "bar": 1.1},
)
Env Wrapper Example
Run examples/run_env_wrapper.py.
import loopquest
loopquest.init()
env = loopquest.make_env("MountainCarContinuous-v0", render_mode="rgb_array")
obs, info = env.reset()
for i in range(100):
action = env.action_space.sample()
obs, reward, terminated, truncated, info = env.step(action)
rgb_array = env.render()
if terminated or truncated:
break
env.close()
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