Antonio Serrano Muñoz
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Add README
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README.md
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---
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library_name: skrl
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tags:
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- deep-reinforcement-learning
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- reinforcement-learning
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- skrl
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model-index:
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- name: PPO
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results:
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- metrics:
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- type: mean_reward
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value: 497.72 +/- 0.79
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name: Total reward (mean)
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task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: Isaac-Cartpole-v0
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type: Isaac-Cartpole-v0
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---
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# IsaacOrbit-Isaac-Cartpole-v0-PPO
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Trained agent model for [NVIDIA Isaac Orbit](https://github.com/NVIDIA-Omniverse/Orbit) environment
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- **Task:** Isaac-Cartpole-v0
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- **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/modules/skrl.agents.ppo.html)
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# Usage (with skrl)
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```python
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from skrl.utils.huggingface import download_model_from_huggingface
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# assuming that there is an agent named `agent`
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path = download_model_from_huggingface("skrl/IsaacOrbit-Isaac-Cartpole-v0-PPO")
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agent.load(path)
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```
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# Hyperparameters
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```python
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# https://skrl.readthedocs.io/en/latest/modules/skrl.agents.ppo.html#configuration-and-hyperparameters
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cfg_ppo = PPO_DEFAULT_CONFIG.copy()
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cfg_ppo["rollouts"] = 16 # memory_size
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cfg_ppo["learning_epochs"] = 8
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cfg_ppo["mini_batches"] = 1 # 16 * 512 / 8192
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cfg_ppo["discount_factor"] = 0.99
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cfg_ppo["lambda"] = 0.95
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cfg_ppo["learning_rate"] = 3e-4
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cfg_ppo["learning_rate_scheduler"] = KLAdaptiveRL
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cfg_ppo["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
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cfg_ppo["random_timesteps"] = 0
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cfg_ppo["learning_starts"] = 0
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cfg_ppo["grad_norm_clip"] = 1.0
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cfg_ppo["ratio_clip"] = 0.2
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cfg_ppo["value_clip"] = 0.2
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cfg_ppo["clip_predicted_values"] = True
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cfg_ppo["entropy_loss_scale"] = 0.0
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cfg_ppo["value_loss_scale"] = 2.0
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cfg_ppo["kl_threshold"] = 0
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cfg_ppo["rewards_shaper"] = None
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cfg_ppo["state_preprocessor"] = RunningStandardScaler
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cfg_ppo["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
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cfg_ppo["value_preprocessor"] = RunningStandardScaler
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cfg_ppo["value_preprocessor_kwargs"] = {"size": 1, "device": device}
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# logging to TensorBoard and writing checkpoints
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cfg_ppo["experiment"]["write_interval"] = 16
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cfg_ppo["experiment"]["checkpoint_interval"] = 80
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```
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