Here’s a small video of our latest checkpoint. We’ve previously run into problems where the robot would learn some very limited behaviors within one training checkpoint. Ex. you’d train a single checkpoint, and it’s behavior would collapse so it would only drive backwards, or only turn left, or only follow a human within an outdoor environment (with fewer cluttered objects). This checkpoint (sac-peachy-resonance-379-21504), is the first to have a variety of behavior depending on the situation.
- Reduced everything down to 2Hz update rate, so that we don’t rattle the pan-tilt module to pieces.
- Fixed bug with dropout being too high in training. (Now we can set dropout for different parts of the observation space independently.)
- Discovered some LR schedules and rates which help prevent Q function collapse.
- Up next: Being able to pass an N-element observation history to the network, so it can start to gain a memory.