Update README.md

README.md

@@ -1,3 +1,94 @@
----
-license: mit
----
+---
+library_name: stable-baselines3
+tags:
+- FetchReachDense-v4
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: DDPG
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: FetchReachDense-v4
+      type: FetchReachDense-v4
+    metrics:
+    - type: mean_reward
+      value: -0.65 +/- 0.39
+      name: mean_reward
+      verified: false
+license: mit
+language:
+- en
+---
+
+# **DDPG** Agent playing **FetchReachDense-v4**
+- [Github Repository](https://github.com/kuds/rl-fetch)
+- [Google Colab Notebook](https://colab.research.google.com/github/kuds/rl-fetch/blob/main/Fetch/Reach/%5BFetch%20Reach%5D%20Deep%20Deterministic%20Policy%20Gradient%20(DDPG).ipynb)
+- [Finding Theta - Blog Post](https://www.findingtheta.com/blog/mastering-robotic-manipulation-with-reinforcement-learning-tqc-and-ddpg-for-fetch-environments)
+
+
+Then, you can load the model using the following Python code:
+
+```python
+import gymnasium as gym
+from stable_baselines3 import DDPG
+from stable_baselines3.common.env_util import make_vec_env
+
+gymnasium.register_envs(gymnasium_robotics)
+
+# Load the trained model
+model = DDPG.load("best-model.zip")
+
+# Create the environment
+env = make_vec_env("FetchReachDense-v4", n_envs=1)
+
+# Reset the environment
+obs, info = env.reset()
+
+# Enjoy the trained agent
+for _ in range(1000):
+    action, _states = model.predict(obs, deterministic=True)
+    obs, rewards, terminated, truncated, info = env.step(action)
+    if terminated or truncated:
+        obs, info = env.reset()
+    env.render()
+env.close()
+```
+
+### Hugging Face Hub
+
+You can also use the Hugging Face Hub to load the model. First, you need to install the Hugging Face Hub library:
+
+```bash
+pip install huggingface_hub
+```
+
+Then, you can load the model from the hub using the following code:
+
+```python
+from huggingface_hub import hf_hub_download
+import torch as th
+from stable_baselines3 import DDPG
+from stable_baselines3.common.env_util import make_vec_env
+
+gymnasium.register_envs(gymnasium_robotics)
+
+# Download the model from the Hub
+model_path = hf_hub_download(repo_id="kuds/fetch-reach-ddpg", filename="best-model.zip")
+
+# Load the model
+model = DDPG.load(model_path)
+
+# Create the environment
+env = make_vec_env("FetchReachDense-v4", n_envs=1)
+
+# Enjoy the trained agent
+obs = env.reset()
+for i in range(1000):
+    action, _states = model.predict(obs, deterministic=True)
+    obs, rewards, dones, info = env.step(action)
+    env.render("human")
+```