RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

Understand

Deep reinforcement learning (deep RL) has been successful in learning sophisticated behaviors automatically; however, the learning process requires a huge number of trials.

• In contrast, animals can learn new tasks in just a few trials, benefiting from their prior knowledge about the world.
• This paper seeks to bridge this gap.
• Rather than designing a "fast" reinforcement learning algorithm, we propose to represent it as a recurrent neural network (RNN) and learn it from data.