Update README.md

README.md

@@ -4,7 +4,11 @@ tags:
 - PandaReachDense-v3
 - deep-reinforcement-learning
 - reinforcement-learning
+- robotics
 - stable-baselines3
+- gymnasium
+- panda-gym
+
 model-index:
 - name: A2C
   results:
@@ -21,17 +25,103 @@ model-index:
       verified: false
 ---
 
-# **A2C** Agent playing **PandaReachDense-v3**
-This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
-using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+# A2C Agent for PandaReachDense-v3
+
+This repository contains a trained **Advantage Actor-Critic (A2C)** agent for the **PandaReachDense-v3** robotics environment from Panda-Gym.
+
+The agent was trained using:
+- Stable-Baselines3
+- Gymnasium
+- Panda-Gym
+
+## Environment
+
+The task involves controlling a Franka Panda robotic arm to reach a target position in 3D space.
+
+Environment:
+- PandaReachDense-v3
+
+Frameworks:
+- Stable-Baselines3
+- Gymnasium
+- Panda-Gym
+
+---
+
+## Training Details
+
+Algorithm:
+- A2C (Advantage Actor-Critic)
+
+Observation Space:
+- Continuous
+
+Action Space:
+- Continuous robotic control
+
+Reward Type:
+- Dense reward
+
+Evaluation Reward:
+- Mean Reward: `-17.94 +/- 6.03`
+
+---
+
+## Usage
 
-## Usage (with Stable-baselines3)
-TODO: Add your code
+Install dependencies:
 
+```bash
+pip install stable-baselines3 gymnasium panda-gym huggingface_sb3
+```
+
+Load the model:
 
 ```python
-from stable_baselines3 import ...
+import gymnasium as gym
+from stable_baselines3 import A2C
 from huggingface_sb3 import load_from_hub
 
-...
+repo_id = "nirmanpatel/a2c-PandaReachDense-v3"
+filename = "a2c-PandaReachDense-v3.zip"
+
+checkpoint = load_from_hub(
+    repo_id=repo_id,
+    filename=filename,
+)
+
+env = gym.make("PandaReachDense-v3")
+
+model = A2C.load(checkpoint)
+
+obs, info = env.reset()
+
+for _ in range(1000):
+    action, _states = model.predict(obs, deterministic=True)
+    obs, reward, terminated, truncated, info = env.step(action)
+
+    if terminated or truncated:
+        obs, info = env.reset()
 ```
+
+---
+
+## Replay Video
+
+- `agent-step-0-to-step-1000.mp4`
+
+---
+
+## Notes
+
+This project demonstrates:
+- Reinforcement Learning for robotics
+- Continuous control using A2C
+- Gymnasium-compatible RL pipelines
+- Hugging Face model deployment
+
+---
+
+## Author
+
+Created by Nirman Patel