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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: 494.34 +/- 0.87 |
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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: OmniIsaacGymEnvs-Cartpole |
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type: OmniIsaacGymEnvs-Cartpole |
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--- |
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# OmniIsaacGymEnvs-Cartpole-PPO |
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Trained agent model for [NVIDIA Omniverse Isaac Gym](https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs) environment |
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- **Task:** Cartpole |
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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/OmniIsaacGymEnvs-Cartpole-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_agent = PPO_DEFAULT_CONFIG.copy() |
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cfg_agent["rollouts"] = 16 # memory_size |
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cfg_agent["learning_epochs"] = 8 |
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cfg_agent["mini_batches"] = 1 # 16 * 512 / 8192 |
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cfg_agent["discount_factor"] = 0.99 |
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cfg_agent["lambda"] = 0.95 |
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cfg_agent["learning_rate"] = 3e-4 |
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cfg_agent["learning_rate_scheduler"] = KLAdaptiveRL |
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cfg_agent["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008} |
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cfg_agent["random_timesteps"] = 0 |
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cfg_agent["learning_starts"] = 0 |
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cfg_agent["grad_norm_clip"] = 1.0 |
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cfg_agent["ratio_clip"] = 0.2 |
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cfg_agent["value_clip"] = 0.2 |
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cfg_agent["clip_predicted_values"] = True |
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cfg_agent["entropy_loss_scale"] = 0.0 |
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cfg_agent["value_loss_scale"] = 2.0 |
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cfg_agent["kl_threshold"] = 0 |
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cfg_agent["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.1 |
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cfg_agent["state_preprocessor"] = RunningStandardScaler |
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cfg_agent["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} |
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cfg_agent["value_preprocessor"] = RunningStandardScaler |
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cfg_agent["value_preprocessor_kwargs"] = {"size": 1, "device": device} |
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# logging to TensorBoard and writing checkpoints |
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cfg_agent["experiment"]["write_interval"] = 16 |
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cfg_agent["experiment"]["checkpoint_interval"] = 80 |
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``` |
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