metadata
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:
pip install stable-baselines3 gymnasium panda-gym huggingface_sb3
Load the model:
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