RL Lab / experiments
Experiment records with hypotheses, algorithms, configurations, observed outcomes, and explicit boundaries on what the evidence demonstrates.
RL-002 · 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 · 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 · complete
Built a Deep RL Lunar Lander agent and recorded an agent replay as qualitative evidence of the learned behavior.