---
library_name: stable-baselines3
tags:
- MountainCar-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: MountainCar-v0
      type: MountainCar-v0
    metrics:
    - type: mean_reward
      value: -116.20 +/- 1.83
      name: mean_reward
      verified: false
---

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

# Model Details
- Model Name: ppo-MountainCar-v0
- Model Type: Proximal Policy Optimization (PPO)
- Policy Architecture: MultiLayerPerceptron (MLPPolicy)
- Environment: MountainCar-v0
- Training Data: The model was trained using three consecutive training sessions:
  - First training session: Total timesteps = 1,000,000
  - Second training session: Total timesteps = 500,000
  - Third training session: Total timesteps = 500,000

# Model Parameters
```python
- n_steps: 2048
- batch_size: 64
- n_epochs: 8
- gamma: 0.999
- gae_lambda: 0.95
- ent_coef: 0.01
- max_grad_norm: 0.5
- Verbose: Enabled (Verbose level = 1)
```