---
env_name: Reacher-v5
tags:
- Reacher-v5
- ppo
- reinforcement-learning
- custom-implementation
- mujoco
- ddp
model-index:
- name: PPO-DDP-ReacherV5
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: Reacher-v5
      type: Reacher-v5
    metrics:
    - type: mean_reward
      value: -5.30 +/- 1.20
      name: mean_reward
      verified: false
---

# **PPO** Agent playing **Reacher-v5**
This is a trained model of a **PPO** agent playing **Reacher-v5**.

## Usage
### create the conda env in https://github.com/GeneHit/drl_practice
```bash
conda create -n drl python=3.12
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/PPO-DDP-ReacherV5", filename="full_model.pt")

# Create the environment. 
env = gym.make("Reacher-v5")
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.