Hi Alex, cool stuff! A clarification from Section 3.2 on coordinating between rollout workers and policy workers: you say that we want k/2 > t_inf / t_env for m

Hi Alex, cool stuff! A clarification from Section 3.2 on coordinating between rollout workers and policy workers: you say that we want k/2 > t_inf / t_env for maximal performance. If t_env is defined as the time required for one thread to simulate one step, it seems like we want k/2 > N * t_inf / t_env, where N is the number of cores/threads on the machine. For example, say t_inf = 10 microseconds, t_env = 1 microsecond and we are training on a 30 core machine. When k=600, we batch 300 inference steps in 10 mics, and complete 300 simulation steps on the other half of the rollout workers in 10 mics. Both cohorts of rollout workers finish at the same time, achieving optimal performance. Am I thinking about this correctly? Also, this equation assumes that we can batch up k/2 inference jobs on the GPU without increasing t_inf correct?