Worker: worker.py¶
Deep reinforcement learning (DRL) employs a trial-and-error manner to collect training data (transitions) from agent-environment interactions, along with the learning procedure. ElegantRL utilizes Worker
to generate transitions and achieves worker parallelism, thus greatly speeding up the data collection.