---
env_name: LunarLander-v3
tags:
- LunarLander-v3
- a2c-gae
- reinforcement-learning
- custom-implementation
- policy-gradient
- pytorch
- a2c
- gae
model-index:
- name: A2C-GAE-LunarLanderV3
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: LunarLander-v3
      type: LunarLander-v3
    metrics:
    - type: mean_reward
      value: 250.02 +/- 55.89
      name: mean_reward
      verified: false
---

# **A2C-GAE** Agent playing **LunarLander-v3**
This is a trained model of a **A2C-GAE** agent playing **LunarLander-v3**.

## 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/A2C-GAE-LunarLanderV3", filename="full_model.pt")

# Create the environment. 
env = gym.make("LunarLander-v3")
state, _ = env.reset()
action = model.action(state)
...
```
There is also a state dict version of the model, you can check the corresponding definition in the repo.