---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- robotics
- stable-baselines3
- gymnasium
- panda-gym

model-index:
- name: A2C
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: PandaReachDense-v3
      type: PandaReachDense-v3
    metrics:
    - type: mean_reward
      value: -17.94 +/- 6.03
      name: mean_reward
      verified: false
---

# 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

Install dependencies:

```bash
pip install stable-baselines3 gymnasium panda-gym huggingface_sb3
```

Load the model:

```python
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()
```

---

## 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