
<!-- ---
torch: 61.68 +/- 2.18
jax: 61.31 +/- 1.39
numpy: 59.62 +/- 1.85
--- -->

# IsaacGymEnvs-Anymal-PPO

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

- **Task:** Anymal
- **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/IsaacGymEnvs-Anymal-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/IsaacGymEnvs-Anymal-PPO", filename="agent.pickle")
    agent.load(path)
    ```

# Hyperparameters

Note: Undefined parameters keep their values by default.

```python
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 24  # memory_size
cfg["learning_epochs"] = 5
cfg["mini_batches"] = 3  # 24 * 4096 / 32768
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"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = None
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": "IsaacGymEnvs-Anymal", "type": "IsaacGymEnvs-Anymal"}, "metrics": [{"type": "mean_reward", "value": "61.68 +/- 2.18", "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 




# IsaacGymEnvs-Anymal-PPO

Trained agent for NVIDIA Isaac Gym Preview environments.

- Task: Anymal
- Agent: PPO

# Usage (with skrl)

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

* PyTorch

    

* JAX

    

# Hyperparameters

Note: Undefined parameters keep their values by default.



# IsaacGymEnvs-Anymal-PPO

Trained agent for NVIDIA Isaac Gym Preview environments.

- Task: Anymal
- Agent: PPO # Usage (with skrl)

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

* PyTorch

    

* JAX # Hyperparameters

Note: Undefined parameters keep their values by default.
TAGS
#skrl #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # IsaacGymEnvs-Anymal-PPO

Trained agent for NVIDIA Isaac Gym Preview environments.

- Task: Anymal
- Agent: PPO # Usage (with skrl)

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

* PyTorch

    

* JAX # Hyperparameters

Note: Undefined parameters keep their values by default.
TAGS
#skrl #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# IsaacGymEnvs-Anymal-PPO

Trained agent for NVIDIA Isaac Gym Preview environments.

- Task: Anymal
- Agent: PPO# Usage (with skrl)

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

* PyTorch

    

* JAX# Hyperparameters

Note: Undefined parameters keep their values by default.