From 52b40824a05cfda6e99fa8d3d7024c15d2042de1 Mon Sep 17 00:00:00 2001 From: Stone Tao Date: Wed, 6 Mar 2024 18:39:27 -0800 Subject: [PATCH] Update custom_tasks.md --- docs/source/user_guide/tutorials/custom_tasks.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/user_guide/tutorials/custom_tasks.md b/docs/source/user_guide/tutorials/custom_tasks.md index 5b1051d13..45afb1144 100644 --- a/docs/source/user_guide/tutorials/custom_tasks.md +++ b/docs/source/user_guide/tutorials/custom_tasks.md @@ -31,7 +31,7 @@ def __init__(self, *args, robot_uids="panda", reconfigure_freq=1, **kwargs): A `reconfiguration_freq` value of 1 means every during every reset we reconfigure. A `reconfiguration_freq` of `k` means every `k` resets we reconfigure. A `reconfiguration_freq` of 0 (the default) means we never reconfigure again. -In general the +In general one use case of setting a positive `reconfiguration_freq` value is for when you want to simulate a task in parallel where each parallel environment is working with a different object/articulation and there are way more object variants than number of parallel environments. For machine learning / RL workflows, setting `reconfiguration_freq` to e.g. 10 ensures every 10 resets the objects being simulated on are randomized which can diversify the data collected for online training. ## Initialization / Randomization