A Unity-based Air Hockey simulation leveraging ML-Agents for reinforcement learning agent training and inference.

Agentic Python pipeline that ingests UK court judgments, extracts POCA 2002 intelligence via Gemini function calling, and surfaces AML conviction patterns by SIC code.

Multi-agent reinforcement learning in Unity’s Soccer Twos environment using POCA. Features enhanced observation memory, custom reward shaping, and optimized training configurations. Analyzes ELO performance, computational efficiency, and training trade-offs. Based on Dennis Soemers’ ML-Agents fork.

Tool for exporting JSON data for further processing with PDFLib POCA.