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Upstream: https://github.com/michaelgillett/mjlab Upstream-Commit: c19f713c415a699a79d71cd96aa13c3104a05047 Upstream-Branch: main
42 lines
1.1 KiB
Python
42 lines
1.1 KiB
Python
"""Tests for CurriculumManager."""
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from unittest.mock import Mock
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import pytest
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import torch
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from mjlab.managers.curriculum_manager import CurriculumManager, CurriculumTermCfg
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@pytest.fixture
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def mock_env():
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env = Mock()
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env.num_envs = 2
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return env
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def test_get_active_iterable_terms_handles_dict_and_scalar_state(mock_env):
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"""Dict- and scalar-shaped curriculum states both yield flat value lists.
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Regression: the dict branch previously indexed `terms` (a list) by term
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name, raising TypeError. Only observable through callers that invoke
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get_active_iterable_terms, which no in-tree caller currently does.
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"""
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def dict_state_func(env, env_ids):
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return {"a": torch.tensor(1.5), "b": 2.0}
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def scalar_state_func(env, env_ids):
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return torch.tensor(7.0)
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cfg = {
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"dict_term": CurriculumTermCfg(func=dict_state_func, params={}),
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"scalar_term": CurriculumTermCfg(func=scalar_state_func, params={}),
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}
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manager = CurriculumManager(cfg, mock_env)
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manager.compute()
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terms = dict(manager.get_active_iterable_terms(0))
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assert terms["dict_term"] == [1.5, 2.0]
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assert terms["scalar_term"] == [7.0]
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