The ELI5 definition for Reinforcement Learning would be training a model to perform better by iteratively learning from its previous mistakes. Reinforcement learning provides a framework for agents to solve problems in case of real-world scenarios. They are able to learn rules (or policies) to solve specific problems, but one of the major limitations of these agents are that they are unable to generalize the learned policy to newer problems. A previously learned rule would cater to a specific problem only, and would often be useless for other (even similar) cases.

A good meta-learning model on the other hand, is expected to generalize to new tasks or environments that have not been encountered by the model in training. The process of adaption to this new environment can be termed a _mini learning session _and happens with testing with limited exposure to newer configuration. In the absence of explicitly fine-tuning models, it is observed that meta-learning is able to autonomously adjust internal states to generalize to newer environments.

Meta-Reinforcement Learning is just Meta-Learning applied to Reinforcement Learning

Furthermore, Wang et al. described meta-RL as “the special category of meta-learning that use recurrent models, applied to RL”, which seems like a much more comprehensive definition than the one above.

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