Update README.md

README.md

@@ -1,217 +1,98 @@
-# :scroll:Loopquest
+# 🚀 **Loopquest**
 
-A Production Tool for Embodied AI.
-![loopquest demo](screenshots/open_loopquest_demo.gif)
+*A Production Tool for Embodied AI.*
 
-- :video_camera:[Quickstart Demo](https://capture.dropbox.com/embed/CpS8Y4g21ClHlief), [Dataset Demo](https://capture.dropbox.com/AOF5rGxHWyRb9T58)
-- :house:[Discord](https://discord.gg/FTnFYeSy9r)
+![Loopquest Demo](screenshots/open_loopquest_demo.gif)
 
-# 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.
-
-```python
-env = gymnasium.make("MountainCarContinuous-v0", render_mode="rgb_array")
-```
-
-->
-
-```python
-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:
-
-```python
-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()
+## 📌 **Major Features**
 
-
-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:
+✔️ **Imitation Learning & Offline Reinforcement Learning MLOps**
+   - Log observation, action, reward, and rendered images with **just one extra line of code**.
 
 ```python
 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](examples/Dataset%20Quickstart.ipynb) for more details.
-
-```python
-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
-
-```python
-{
-    '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>]
-}
+env = loopquest.make_env("MountainCarContinuous-v0", render_mode="rgb_array")
 ```
 
-- All the regular MLOps features are included, e.g. data visualization, simulation rendering, experiment management.
-
-# Installation
-
-For stable version, run
-
-```
+✔️ **Evaluate Local & Remote Policies**
+   - **Local Policy Evaluation**
+   ```python
+   from loopquest.eval import evaluate_local_policy
+   evaluate_local_policy(policy, ["FetchPickAndPlace-v2"], num_episodes=1)
+   ```
+
+   - **Remote Policy Evaluation**
+   ```python
+   from loopquest.eval import evaluate_remote_policy
+   evaluate_remote_policy("jxx123/ppo-LunarLander-v2", "ppo-LunarLander-v2.zip", "PPO", ["LunarLander-v2"], num_episodes=1)
+   ```
+
+✔️ **Trainable Data for Robotics Foundation Models**
+   - Easily download structured datasets with **TensorFlow, PyTorch, or Hugging Face APIs**.
+   ```python
+   from loopquest.datasets import load_dataset
+   ds = load_dataset("your_experiment_id")
+   ```
+
+✔️ **Complete MLOps Features**
+   - **Data visualization**, **simulation rendering**, and **experiment management**.
+
+---
+
+## ⚡ **Installation**
+
+For the stable version:
+```bash
 pip install loopquest
 ```
 
-For dev version or loopquest project contributors, clone the git to your local machine by running
-
-```
+For development or contributions:
+```bash
 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](examples/run_local_eval.py).
+## 🎯 **Quick Start Examples**
 
+🔹 **Run Local Evaluation**
 ```python
-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",
-)
+evaluate_local_policy(policy, ["FetchPickAndPlace-v2"], num_episodes=1)
 ```
 
-Run [examples/run_remote_eval.py](examples/run_remote_eval.py).
-
+🔹 **Run Remote Evaluation**
 ```python
-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},
-)
+evaluate_remote_policy("jxx123/ppo-LunarLander-v2", "ppo-LunarLander-v2.zip", "PPO", ["LunarLander-v2"], num_episodes=1)
 ```
 
-## Env Wrapper Example
-
-Run [examples/run_env_wrapper.py](examples/run_env_wrapper.py).
-
+🔹 **Environment Wrapper Example**
 ```python
 import loopquest
-
-loopquest.init()
 env = loopquest.make_env("MountainCarContinuous-v0", render_mode="rgb_array")
 obs, info = env.reset()
-for i in range(100):
+for _ 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()
 ```
+
+---
+
+## 🌎 **Community & Support**
+
+💬 **Join Our Discord:** [Click Here](https://discord.gg/FTnFYeSy9r) 🏡
+🎥 **Watch Quickstart Demos:** [Demo 1](https://capture.dropbox.com/embed/CpS8Y4g21ClHlief) | [Demo 2](https://capture.dropbox.com/AOF5rGxHWyRb9T58)
+
+---
+
+💡 *Loopquest is designed to simplify AI-driven robotics. Give it a try and start building today!* ✨