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Michel Aractingi 2024-11-25 09:51:33 +00:00
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lerobot/common/policies/tdmpc2/configuration_tdmpc2.py
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@ -70,13 +70,9 @@ class TDMPC2Config:
be non-zero. be non-zero.
n_pi_samples: Number of samples to draw from the policy / world model rollout every CEM iteration. Can n_pi_samples: Number of samples to draw from the policy / world model rollout every CEM iteration. Can
be zero. be zero.
uncertainty_regularizer_coeff: Coefficient for the uncertainty regularization used when estimating
trajectory values (this is the λ coeffiecient in eqn 4 of FOWM).
n_elites: The number of elite samples to use for updating the gaussian parameters every CEM iteration. n_elites: The number of elite samples to use for updating the gaussian parameters every CEM iteration.
elite_weighting_temperature: The temperature to use for softmax weighting (by trajectory value) of the elite_weighting_temperature: The temperature to use for softmax weighting (by trajectory value) of the
elites, when updating the gaussian parameters for CEM. elites, when updating the gaussian parameters for CEM.
gaussian_mean_momentum: Momentum (α) used for EMA updates of the mean parameter μ of the gaussian
parameters optimized in CEM. Updates are calculated as μ⁻ ← αμ⁻ + (1-α)μ.
max_random_shift_ratio: Maximum random shift (as a proportion of the image size) to apply to the max_random_shift_ratio: Maximum random shift (as a proportion of the image size) to apply to the
image(s) (in units of pixels) for training-time augmentation. If set to 0, no such augmentation image(s) (in units of pixels) for training-time augmentation. If set to 0, no such augmentation
is applied. Note that the input images are assumed to be square for this augmentation. is applied. Note that the input images are assumed to be square for this augmentation.