Cap DreamWaQ adaptive learning rate
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@@ -3,9 +3,14 @@ from types import SimpleNamespace
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import numpy as np
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import torch
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from motrix_rl.rslrl.torch.train.dreamwaq_ppo import DreamWaQPPO
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from motrix_rl.rslrl.torch.wrap_vec_env import RslrlNpEnvWrap
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def test_dreamwaq_adaptive_learning_rate_is_capped_at_initial_rate():
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assert DreamWaQPPO.max_learning_rate == 1e-3
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def test_step_preserves_rewards_and_marks_only_truncations_as_timeouts():
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state = SimpleNamespace(
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obs=np.zeros((2, 3), dtype=np.float32),
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