facebookresearch / rlmeta
RLMeta is a light-weight flexible framework for Distributed Reinforcement Learning Research.
Summary
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| Main Code: 2,834 LOC (34 files) = PY (72%) + H (16%) + CC (10%) | |||
| Duplication: 5% | |||
| File Size: 0% long (>1000 LOC), 60% short (<= 200 LOC) | |||
| Unit Size: 0% long (>100 LOC), 70% short (<= 10 LOC) | |||
| Conditional Complexity: 0% complex (McCabe index > 50), 94% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (9 components) | ||
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less than a month old
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0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits. |
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| Goals: Keep the system simple and easy to change (4) | |||
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Features of interest:
TODOs
2 files |
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Latest commit date: 2022-01-19
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17
commits
(30 days)
1
contributors
(30 days) |
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