This repository implements a real-time reinforcement learning training system for two classic arcade games: Robotron: 2084 and Tempest. The system uses MAME emulators with Lua scripts to extract game state and apply actions, communicating via TCP sockets to Python agents. The Python side includes multiple RL algorithms: a Rainbow DQN (distributional C51 with factored action heads) for Tempest, and both a DQN and a PPO (Set Transformer) variant for Robotron. Training is supported by prioritized experience replay, n-step returns, behavioral cloning from heuristic experts, and a live metrics dashboard with real-time charts and game preview. The repository also contains the original assembly source code for both games, included for reference but not actively used by the training pipeline. The codebase is functional and well-structured in parts, but suffers from duplication between the two game projects, legacy code in an '_old_engine' directory, and platform-specific dependencies. It is actively maintained with recent training configuration tweaks documented in notes.