13 Toward artificial general intelligence

 

In this chapter

  • You will look back at the algorithms you learned in this book, as well as learn about deep reinforcement learning methods that weren’t covered in depth.
  • You will learn about advanced deep reinforcement learning techniques that, when combined, allow agents to display more general intelligence.
  • You will 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; however, there’s more to DRL than what we covered in this book, and I want you to have some direction going forward.

What was covered and what notably wasn’t?

Markov decision processes

Planning methods

Bandit methods

Tabular reinforcement learning

Value-based deep reinforcement learning

Policy-based and actor-critic deep reinforcement learning

Advanced actor-critic techniques

Model-based deep reinforcement learning

Derivative-free optimization methods

More advanced concepts toward AGI

What is AGI, again?

Advanced exploration strategies

Inverse reinforcement learning

Transfer learning

Multi-task learning

Curriculum learning

Meta learning

Hierarchical reinforcement learning

Multi-agent reinforcement learning

Explainable AI, safety, fairness, and ethical standards

What happens next?

How to use DRL to solve custom problems

Going forward

Get yourself out there! Now!

Summary

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