Research / RL Lab
RL Laboratory
Engineering notes and a public reinforcement-learning laboratory documenting experiments, learning progression, results, theory, and failure analysis. The evidence is kept separate from claims about aerospace control capability.
Experiments
- RL-002 — Q-Learning Navigation in a 4×4 GridWorld (complete): Trained for 500 episodes with a 16-state, 4-action Q-table. The learned greedy policy routes from state 0 to the goal at state 15 while avoiding obstacle states 5 and 10; the recorded policy corresponds to a 6-move route: 0 → 4 → 8 → 9 → 13 → 14 → 15.
- RL-001 — Tabular Q-Learning on CartPole-v1 (in-progress): The first 500-episode run produced a measurable but weak policy. Mean evaluation reward was 52.0 ± 36.7 with a 0.0% success rate at the project threshold.
- RL-003 — Deep RL Lunar Lander (complete): Built a Deep RL Lunar Lander agent and recorded an agent replay as qualitative evidence of the learned behavior.