Multi-task Deep Reinforcement Learning with PopArt

Understand

The reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks.

• These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent instance.
• This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on.
• In this work, we study the problem of learning to master not one but multiple sequential-decision tasks at once.