v0.1.0 - Deep Reinforcement Learning for Competitive Agents in MicroRTS: Architecture, Training, and Tournament Evaluation

Master's thesis release, UCLouvain, 2026.

This is the v0.1.0 archival release of the thesis pipeline:

• UECD-Best tops a 19-agent IEEE-CoG-style round-robin at 96.67% pool win rate.
• Beats RAISocketAI (IEEE-CoG competition winner) in 65.7% of head-to-head games.
• Trained on 9.47 GPU-days / 350M steps (vs RAISocketAI's 23.6 GPU-days / 500M).
• UECD-MultiMap generalises across 5 layouts of 3 different sizes with no per-map collapse.

The full pipeline (training, evaluation, tournament, behaviour cloning, benchmarks, analysis) is reproducible from one of two automated setup scripts under setup/ (laptop or CECI HPC).

See dissertation/dissertation.pdf for the full thesis and CITATION.cff for citation metadata.

A Zenodo DOI will be assigned automatically once the Zenodo integration archives this tag (10-30 minutes after release).