13 Towards artificial general intelligence

·   You look back at the algorithms you learned in this book, and learn about deep reinforcement learning methods that weren’t covered in-depth.

·   You learn about advanced deep reinforcement learning techniques that, when combined, allow agents to display more-general intelligence.

·  You get my parting advice on how to follow your dreams and contribute to these fabulous fields of artificial intelligence and deep reinforcement learning.

Our ultimate objective is to make programs that learn from their experience as effectively as humans do.

— John McCarthy Founder of the field of Artificial Intelligence Inventor of the Lisp programming Language

In this book, we have surveyed a wide range of decision-making algorithms and reinforcement-learning agents; from the planning methods that you learned about in chapter 3 to the state-of-the-art deep reinforcement learning agents that we covered in the previous chapter. The focus of this book is to teach the ins-and-outs of the algorithms, and I think it does a pretty decent job at that. However, there is more to DRL than what we covered in this book, and I want you to have some direction going forward.

13.1 What was covered, and what notably wasn’t?

13.1.1 Markov Decision Processes

13.1.2 Planning methods

13.1.3 Bandit methods

13.1.4 Tabular reinforcement learning

13.1.5 Value-based deep reinforcement learning

13.1.6 Policy-based and actor-critic deep reinforcement learning

13.1.7 Advanced actor-critic techniques

13.1.8 Model-based deep reinforcement learning

13.1.9 Derivative-free optimization methods

13.2 More advanced concepts towards AGI

13.2.1 What is AGI, again?

13.2.2 Advanced exploration strategies

13.2.3 Inverse reinforcement learning

13.2.4 Transfer learning

13.2.5 Multi-task learning

13.2.6 Curriculum learning

13.2.7 Meta learning

13.2.8 Hierarchical reinforcement learning

13.2.9 Multi-agent reinforcement learning

13.2.10                 Explainable AI, Safety, Fairness, and Ethical Standards

13.3 What happens next?

13.3.1 How to use DRL to solve custom problems?

13.3.2 Going forward

13.3.3 Get yourself out there! Now!

13.4 Summary