
# OmniIsaacGymEnvs-Crazyflie-PPO

Trained agent model for [NVIDIA Omniverse Isaac Gym](https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs) environment

- **Task:** Crazyflie
- **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/modules/skrl.agents.ppo.html)

# Usage (with skrl) 

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

# assuming that there is an agent named `agent`
path = download_model_from_huggingface("skrl/OmniIsaacGymEnvs-Crazyflie-PPO")
agent.load(path)
```

# Hyperparameters

```python
# https://skrl.readthedocs.io/en/latest/modules/skrl.agents.ppo.html#configuration-and-hyperparameters
cfg_agent = PPO_DEFAULT_CONFIG.copy()
cfg_agent["rollouts"] = 16  # memory_size
cfg_agent["learning_epochs"] = 8
cfg_agent["mini_batches"] = 1  # 16 * 512 / 8192
cfg_agent["discount_factor"] = 0.99
cfg_agent["lambda"] = 0.95
cfg_agent["learning_rate"] = 3e-4
cfg_agent["learning_rate_scheduler"] = KLAdaptiveRL
cfg_agent["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg_agent["random_timesteps"] = 0
cfg_agent["learning_starts"] = 0
cfg_agent["grad_norm_clip"] = 1.0
cfg_agent["ratio_clip"] = 0.2
cfg_agent["value_clip"] = 0.2
cfg_agent["clip_predicted_values"] = True
cfg_agent["entropy_loss_scale"] = 0.0
cfg_agent["value_loss_scale"] = 2.0
cfg_agent["kl_threshold"] = 0
cfg_agent["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.1
cfg_agent["state_preprocessor"] = RunningStandardScaler
cfg_agent["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg_agent["value_preprocessor"] = RunningStandardScaler
cfg_agent["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and writing checkpoints
cfg_agent["experiment"]["write_interval"] = 16
cfg_agent["experiment"]["checkpoint_interval"] = 80
```

{"library_name": "skrl", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "skrl"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "OmniIsaacGymEnvs-Crazyflie", "type": "OmniIsaacGymEnvs-Crazyflie"}, "metrics": [{"type": "mean_reward", "value": "1106.75 +/- 63.75", "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 


# OmniIsaacGymEnvs-Crazyflie-PPO

Trained agent model for NVIDIA Omniverse Isaac Gym environment

- Task: Crazyflie
- Agent: PPO

# Usage (with skrl) 



# Hyperparameters



# OmniIsaacGymEnvs-Crazyflie-PPO

Trained agent model for NVIDIA Omniverse Isaac Gym environment

- Task: Crazyflie
- Agent: PPO # Usage (with skrl) # Hyperparameters
TAGS
#skrl #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # OmniIsaacGymEnvs-Crazyflie-PPO

Trained agent model for NVIDIA Omniverse Isaac Gym environment

- Task: Crazyflie
- Agent: PPO # Usage (with skrl) # Hyperparameters
TAGS
#skrl #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# OmniIsaacGymEnvs-Crazyflie-PPO

Trained agent model for NVIDIA Omniverse Isaac Gym environment

- Task: Crazyflie
- Agent: PPO# Usage (with skrl)# Hyperparameters