RL Lab / results
RL Results
Recorded reinforcement-learning measurements and artifacts. Results are labelled according to their scope; benchmark behavior is not treated as proof of aerospace performance.
Recorded measurements
RL-002 — Q-Learning Navigation in a 4×4 GridWorld
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.
- Grid size: 4 × 4
- States: 16
- Actions: 4
- Training episodes: 500
- Greedy route: 6 moves
RL-001 — Tabular Q-Learning on CartPole-v1
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.
- Mean evaluation reward: 52.0 ± 36.7
- Best training episode: 191
- Episodes to threshold: Not reached
- Training runs: 1
- Success rate: 0.0%
RL-003 — Deep RL Lunar Lander
Built a Deep RL Lunar Lander agent and recorded an agent replay as qualitative evidence of the learned behavior.
- Environment: LunarLander
- Method: Deep RL
- Course stage: HF Unit 1
- Replay: Recorded
- Benchmark: Not recorded