墨子 × MADDPG - Projects

Experimental conditions and comparisons

The client commissioned graduate-level simulation research implementation. Algorithms, environment variability, training budgets, and comparison conditions needed to be organized together. I used those conditions to structure the research engineering so implementation, configuration, and analysis could be read in context.

A simulation result depends on its conditions. Research structure reconstructed from retained materials; not a Mozi interface or experimental performance result. — Results need conditions, versions, and original records. Research-structure illustration.

The roles of three versions

Version 1 retains local training, comparison, and analysis for basic and extended methods. Version 2 explores a more complex multi-agent environment. Version 3 adds a Mozi adaptation layer on the basic version. These are validation, complexity exploration, and integration tasks; version numbers alone do not imply improving performance.

Three versions serve three research tasks. Research structure reconstructed from retained materials; not a Mozi interface or experimental performance result. — Local experiments, complexity exploration, and platform adaptation are different tasks, not a performance ranking.

Training, comparisons, and records

Training coordination, configurations, comparisons, analysis, and saving entry points connect the implementation into a research workflow. They help define which conditions belong to a result. Historical curves, logs, and weights have not been recovered; diagrams explain the code structure without recreating missing experimental outcomes.

Make the experiment workflow inspectable. Research structure reconstructed from retained materials; not a Mozi interface or experimental performance result. — Implementation organizes an experiment workflow; diagrams do not substitute for historical results.

Mozi platform adaptation

The Mozi version retains an adapter, environment documentation, trainers, and model-loading scripts. External SDKs, services, and runtime conditions remain necessary for platform operation. The engineering work makes those dependencies and interfaces explicit, with successful platform runs requiring corresponding integration records.

Platform adaptation is its own engineering task. Research structure reconstructed from retained materials; not a Mozi interface or experimental performance result. — Platform integration depends on both adaptation code and external runtime conditions. Architecture illustration.

Implementation scope and performance evaluation

The three versions retain 67 Python source files, including 25 in test directories. Static syntax checks describe the source material, while training, test execution, and platform integration require their own verification. Code explains the research structure; original records and consistent comparisons must support performance conclusions.

Implementation scope and performance need different evidence. Research structure reconstructed from retained materials; not a Mozi interface or experimental performance result. — Static parsing and source counts describe materials, without establishing training or platform acceptance.

My work and delivery

I undertook the client commission, organized local experiments, comparisons, complex-environment exploration, and platform adaptation, and completed delivery. Historical notes preserve limited training budgets and explorations without clear gains. My scope is engineering implementation and delivery; client paper authorship and specific performance conclusions retain separate evidence requirements.

My contribution: research engineering and delivery. Research structure reconstructed from retained materials; not a Mozi interface or experimental performance result. — Research judgment preserves constraints and inconclusive explorations. Research-structure illustration.