
<!-- ---
torch: 497.66 +/- 0.47
jax: 497.41 +/- 0.0
numpy: 
--- -->

# IsaacOrbit-Isaac-Cartpole-v0-PPO

Trained agent for [NVIDIA Isaac Orbit](https://github.com/NVIDIA-Omniverse/Orbit) environments.

- **Task:** Isaac-Cartpole-v0
- **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html)

# Usage (with skrl)

Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts.

* PyTorch

    ```python
    from skrl.utils.huggingface import download_model_from_huggingface

    # assuming that there is an agent named `agent`
    path = download_model_from_huggingface("skrl/IsaacOrbit-Isaac-Cartpole-v0-PPO", filename="agent.pt")
    agent.load(path)
    ```

* JAX

    ```python
    from skrl.utils.huggingface import download_model_from_huggingface

    # assuming that there is an agent named `agent`
    path = download_model_from_huggingface("skrl/IsaacOrbit-Isaac-Cartpole-v0-PPO", filename="agent.pickle")
    agent.load(path)
    ```

# Hyperparameters

```python
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16  # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 1  # 16 * 512 / 8192
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = None
cfg["time_limit_bootstrap"] = True
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
```

{"library_name": "skrl", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "skrl"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Isaac-Cartpole-v0", "type": "Isaac-Cartpole-v0"}, "metrics": [{"type": "mean_reward", "value": "497.66 +/- 0.47", "name": "Total reward (mean)"}]}]}]}
skrl deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#skrl #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 




# IsaacOrbit-Isaac-Cartpole-v0-PPO

Trained agent for NVIDIA Isaac Orbit environments.

- Task: Isaac-Cartpole-v0
- Agent: PPO

# Usage (with skrl)

Note: Visit the skrl Examples section to access the scripts.

* PyTorch

    

* JAX

    

# Hyperparameters



# IsaacOrbit-Isaac-Cartpole-v0-PPO

Trained agent for NVIDIA Isaac Orbit environments.

- Task: Isaac-Cartpole-v0
- Agent: PPO # Usage (with skrl)

Note: Visit the skrl Examples section to access the scripts.

* PyTorch

    

* JAX # Hyperparameters
TAGS
#skrl #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # IsaacOrbit-Isaac-Cartpole-v0-PPO

Trained agent for NVIDIA Isaac Orbit environments.

- Task: Isaac-Cartpole-v0
- Agent: PPO # Usage (with skrl)

Note: Visit the skrl Examples section to access the scripts.

* PyTorch

    

* JAX # Hyperparameters
TAGS
#skrl #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# IsaacOrbit-Isaac-Cartpole-v0-PPO

Trained agent for NVIDIA Isaac Orbit environments.

- Task: Isaac-Cartpole-v0
- Agent: PPO# Usage (with skrl)

Note: Visit the skrl Examples section to access the scripts.

* PyTorch

    

* JAX# Hyperparameters