Learning Policies For Learning Policies -- Meta Reinforcement Learning (RL²) in Tensorflow

Reinforcement Learning provides a framework for training agents to solve problems in the world. One of the limitations of these agents however is their inflexibility once trained. They are able to learn a policy to solve a specific problem (formalized as an MDP), but that learned policy is often useless in new problems, even relatively similar ones. Imagine the simplest possible agent: one trained to solve a two-armed bandit task in which one arm always provides a positive reward, and the other arm always provides no reward. Using any RL algorithm such as Q-Learning or Policy Gradient, the agent can quickly learn to always choose the arm with the positive reward.