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Update 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"] = 16 # memory_size
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- cfg_ppo["learning_epochs"] = 5
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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"] = 0.95
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- cfg_ppo["learning_rate"] = 5e-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
@@ -56,7 +56,7 @@ 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"] = 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