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README.md
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@@ -41,12 +41,12 @@ agent.load(path)
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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"] =
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cfg_ppo["learning_epochs"] =
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cfg_ppo["mini_batches"] = 4 # 16 * 8192 / 32768
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cfg_ppo["discount_factor"] = 0.99
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cfg_ppo["lambda"] =
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cfg_ppo["learning_rate"] =
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cfg_ppo["learning_rate_scheduler"] = KLAdaptiveRL
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cfg_ppo["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.016}
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cfg_ppo["random_timesteps"] = 0
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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.
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cfg_ppo["kl_threshold"] = 0
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cfg_ppo["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
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cfg_ppo["state_preprocessor"] = RunningStandardScaler
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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"] = 20 # memory_size
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cfg_ppo["learning_epochs"] = 10
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cfg_ppo["mini_batches"] = 4 # 16 * 8192 / 32768
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cfg_ppo["discount_factor"] = 0.99
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cfg_ppo["lambda"] = 1
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cfg_ppo["learning_rate"] = 6e-4
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cfg_ppo["learning_rate_scheduler"] = KLAdaptiveRL
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cfg_ppo["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.016}
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cfg_ppo["random_timesteps"] = 0
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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"] = 2.5
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cfg_ppo["kl_threshold"] = 0
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cfg_ppo["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
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cfg_ppo["state_preprocessor"] = RunningStandardScaler
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