This　article　studies　active　disturbance　rejection　control　(ADRC)　for　uncertain　switched　systems　under　triggered　learning.　The　novel　triggered-learning　ADRC　(TL-ADRC)　framework　optimizes　the　ADRC　performance　of　switched　systems　through　reinforcement　learning　(RL)　and　enables　＂on-demand＂updates　of　neural　networks　with　guidance　from　a　pre-designed　trigger.　The　innovation　of　this　article　is　mainly　reflected　in　four　aspects:　(i)　The　RL-based　gain　automatic　update　mechanism　(i.e.,　the　dual-gain　optimization　mechanism　(DGOM))　optimizes　the　performance　of　extended　state　observer　(ESO)　and　controller;　the　optimal　policy　is　obtained　from　the　experience-based　deep　deterministic　policy　gradient　(DDPG)　with　self-learning　ability.　(ii)　The　adaptive　performance-triggered　strategy　guides　the　update　of　dual-gain;　the　on-demand　triggering　judgment　is　achieved　by　comparing　cost　functions　that　reflect　the　tracking　control　performance.　(iii)　The　proposed　theoretical　analysis　method　proves　that　the　learning　mechanism　can　enhance　the　closed-loop　system　performance.　(iv)　The　constructed　ADRC-based　switching　law　accelerates　the　convergence　of　system　tracking　error.　Finally,　a　comparative　simulation　example　demonstrates　the　effectiveness　of　the　proposed　method.　　©　2025　IEEE.