- Meta-RL is meta-learning on reinforcement learning tasks.
- After trained over a distribution of tasks, the agent is able to solve a new task by developing a new RL algorithm.
- Train: Throw a bunch of problems with the same core structure to the model.
Test: the meta-RL agent will be able to rapidly identify the key parameters of any new problem, eventually achieving optimal performance on this new problem