Fly RL

Puts a virtual fly in procedurally generated rooms and runs the annotated MaleCNS v1.0 graph (167,184 neurons, 25,583,622 directed edges) as a fixed recurrent model between simulated sensors and flight commands. Two controller families are compared: PPO policies and an explicit observed-map planner. Much of the README is a record of failure. Learned policies reach 61/64 goals through one wide gate but 0/64 in the 48 x 48 room with five narrow passages, where only the hand-written planner works (13/16 frozen). It states that navigation success does not establish an advantage from fly wiring.

Official site ↗

#malecns#navigation#ppo#imitation-learning#planner#negative-result