Note, you may have to adjust --train_for_env_steps to a suitably high number as the experiment will resume at the number of steps it concluded at.
Adjustments
This is the best one I have managed to train with sample_factory. I have been training two policies at once and invariably one of the policies is weaker than the other. By increasing the gamma to 0.9999 and using ppo I was able to get this model but it is still far off the ppo benchmark.