Create README.md

README.md

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+---
+library_name: stable-baselines3
+tags:
+- deep-reinforcement-learning
+- reinforcement-learning
+- skrl
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 494.34 +/- 0.87
+      name: Total reward (mean)
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+---
+
+# OmniIsaacGymEnvs-Cartpole-PPO
+
+Trained agent model for [NVIDIA Omniverse Isaac Gym](https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs) environment
+
+- **Task:** Cartpole
+- **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/modules/skrl.agents.ppo.html)
+
+# Usage (with skrl) 
+
+```python
+```
+
+# 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
+```