Implemented the rewards hyperparameters #31
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GiulioRomualdi
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Thank you for the contribution!
| slow_down_factor = 1.0 | ||
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| def __post_init__(self): | ||
| self.algorithm.class_name = "AMP_PPO" No newline at end of file |
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| self.algorithm.class_name = "AMP_PPO" | |
| self.algorithm.class_name = "AMP_PPO" | |
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In some cases, adjusting the ratio of Task reward to Style reward can better control the humanoid robot. Therefore, the hyperparameters marked as TODO in
amp_on_policy_runner.pyhave been implemented in the algorithm's configuration file, and an example has been provided.