The Simulation Trap
Most RL comparisons stop at MuJoCo. Clean physics, deterministic dynamics, unlimited resets. Then you deploy to hardware and PPO's variance suddenly matters. SAC's sample efficiency looks less impressive when each episode takes 4 minutes and your servo overheats after 100 trials.
I ran both algorithms on three real manipulation tasks: peg insertion, door opening, and cable routing. Same reward functions, same hyperparameters (within reason), same hardware budget. The results don't match what you'd expect from simulation benchmarks.
This isn't another "PPO is general-purpose, SAC handles continuous actions" post. This is what happens when your training loop includes motor calibration drift, inconsistent object placement, and a robot that needs 30 seconds to reset between episodes.
Hardware Setup and Why It Matters
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