---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: AntBulletEnv-v0
      type: AntBulletEnv-v0
    metrics:
    - type: mean_reward
      value: 1957.59 +/- 114.55
      name: mean_reward
      verified: false
---

# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).

## Usage (with Stable-baselines3)


```python
import gym
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub

checkpoint = load_from_hub(
    repo_id="AntBulletEnv-v0",
    filename="a2c-AntBulletEnv-v0.zip",
)
model = A2C.load(checkpoint)

# Evaluate the agent and watch it
eval_env = gym.make("AntBulletEnv-v0")
mean_reward, std_reward = evaluate_policy(
    model, eval_env, render=True, n_eval_episodes=5, deterministic=True, warn=False
)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

```