---
env_name: MountainCar-v0
tags:
- MountainCar-v0
- reinforce_rnd
- reinforcement-learning
- custom-implementation
- curiosity
- reinforce
- rnd
- policy-gradient
- pytorch
model-index:
- name: Curiosity-MountainCarV0
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: MountainCar-v0
      type: MountainCar-v0
    metrics:
    - type: mean_reward
      value: -114.75 +/- 11.42
      name: mean_reward
      verified: false
---

# **Reinforce_RND** Agent playing **MountainCar-v0**
This is a trained model of a **Reinforce_RND** agent playing **MountainCar-v0**.

## Usage
### create the conda env in https://github.com/GeneHit/drl_practice
```bash
conda create -n drl python=3.10
conda activate drl
python -m pip install -r requirements.txt
```

### play with full model
```python
# load the full model
model = load_from_hub(repo_id="winkin119/Curiosity-MountainCarV0", filename="full_model.pt")

# Create the environment. 
env = gym.make("MountainCar-v0")
state, _ = env.reset()
action = model.action(state)
...
```
There is also a state dict version of the model, you can check the corresponding chapter in the repo.