---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: PandaReachDense-v3
      type: PandaReachDense-v3
    metrics:
    - type: mean_reward
      value: -0.25 +/- 0.11
      name: mean_reward
      verified: false
---

# **SAC** Agent playing **PandaReachDense-v3**
This is a trained model of a **SAC** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).

## Usage (with Stable-baselines3)

Copy the code:

```python
from stable_baselines3 import SAC

model = SAC("MultiInputPolicy", env, learning_rate = 0.00073, gamma = 0.98, gradient_steps = 64, verbose=1)
model.learn(5_000)
```