---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
- a2c
- robotics
model-index:
- name: A2C
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: PandaReachDense-v3
      type: PandaReachDense-v3
    metrics:
    - type: mean_reward
      value: -0.35 +/- 0.12
      name: mean_reward
      verified: false
---

# **A2C** Agent playing **PandaReachDense-v3**

This is a trained model of an **A2C (Advantage Actor-Critic)** agent playing **PandaReachDense-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and [Panda-Gym](https://github.com/qgallouedec/panda-gym).

## Environment Description

The **PandaReachDense-v3** environment features a Franka Emika Panda robotic arm that must place its end-effector at a target position (green ball). This is a continuous control task with:
- **Observation space**: Dictionary containing achieved_goal, desired_goal, and observation (position + velocity)
- **Action space**: 3-dimensional continuous control (x, y, z displacement)
- **Reward**: Dense reward based on distance to target

## Training Results

| Metric | Value |
|--------|-------|
| Mean Reward | -0.35 |
| Std Reward | ± 0.12 |
| Evaluation Episodes | 10 |

## Hyperparameters