---
tags:
- LunarLander-v2
- reinforcement-learning
- rl-framework
model-index:
- name: PPO-LunarLander-v2-v3
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: LunarLander-v2
      type: LunarLander-v2
    metrics:
    - type: mean_reward
      value: -192.82 +/- 29.17
      name: mean_reward
      verified: false
---


# PPO agent playing on *LunarLander-v2*

This is a trained model of an agent playing on the environment *LunarLander-v2*.
The agent was trained with a PPO algorithm and evaluated for 100 episodes.
See further agent and evaluation metadata in the according README section.


## Import
The Python module used for training and uploading/downloading is [rl-framework](https://github.com/alexander-zap/rl-framework).
It is an easy-to-read, plug-and-use Reinforcement Learning framework and provides standardized interfaces
and implementations to various Reinforcement Learning methods and environments.

Also it provides connectors for the upload and download to popular model version control systems,
including the HuggingFace Hub.

## Usage
```python

from rl-framework import StableBaselinesAgent, StableBaselinesAlgorithm

# Create new agent instance
agent = StableBaselinesAgent(
    algorithm=StableBaselinesAlgorithm.PPO
    algorithm_parameters={
        ...
    },
)

# Download existing agent from HF Hub
repository_id = "zap-thamm/PPO-LunarLander-v2-v3"
file_name = "algorithm.zip"
agent.download(repository_id=repository_id, filename=file_name)

```

Further examples can be found in the [exploration section of the rl-framework repository](https://github.com/alexander-zap/rl-framework/tree/main/exploration).