Conclusion
In this chapter, we covered meta-reinforcement learning, one of the most important research directions in RL as its promise is to train agents that can adapt to new environments very quickly. To this end, we covered three methods: Recurrent policies, gradient-based, and partial observability-based. Currently, meta-RL is at its infancies and not performing as well as the more established approaches, so we covered the challenges in this area as well.
In the next chapter, we will cover several advanced topics in a single chapter. So, stay tuned to further deepen your RL expertise.
