47372443ff
Upstream: https://github.com/pollen-robotics/microduck_rl Upstream-Commit: d424a0c899f6b33cbd3daeb279913134349c0b63 Upstream-Branch: develop
49 lines
1.2 KiB
Python
49 lines
1.2 KiB
Python
"""descent_speed_reward : récompense la vitesse d'avance vers le bas de la pente
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(monde +x), plafonnée à `cap`, nulle si le robot recule/remonte, NaN-safe.
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"""
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import torch
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from mjlab_microduck.tasks.mdp import descent_speed_reward
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class _Data:
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def __init__(self, vx):
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self.root_link_lin_vel_w = torch.tensor(vx, dtype=torch.float32).reshape(-1, 1).repeat(1, 3)
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# seule la colonne 0 (x) est lue ; on met vx en x
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self.root_link_lin_vel_w[:, 0] = torch.tensor(vx, dtype=torch.float32)
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class _Asset:
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def __init__(self, data):
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self.data = data
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class _Env:
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def __init__(self, vx):
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self._a = _Asset(_Data(vx))
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self.scene = self
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def __getitem__(self, _k):
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return self._a
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def test_rewards_forward_speed_up_to_cap():
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out = descent_speed_reward(_Env([0.5]), cap=0.8)
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assert abs(float(out[0]) - 0.5) < 1e-6
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def test_caps_high_speed():
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out = descent_speed_reward(_Env([1.5]), cap=0.8)
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assert abs(float(out[0]) - 0.8) < 1e-6
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def test_zero_for_backward_or_uphill():
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out = descent_speed_reward(_Env([-0.4]), cap=0.8)
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assert float(out[0]) == 0.0
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def test_nan_safe():
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out = descent_speed_reward(_Env([float("nan")]), cap=0.8)
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assert float(out[0]) == 0.0
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