Summary

This chapter introduced the Gym Minecraft environment, available at https://github.com/tambetm/gym-minecraft. You have learned how to launch a Minecraft mission and how to implement an emulator for it. The most important part of this chapter was the asynchronous reinforcement learning framework. You learned what the shortcomings of DQN are, and why DQN is difficult to apply in complex tasks. Then, you learned how to apply the asynchronous reinforcement learning framework in the actor-critic method REINFORCE, which led us to the A3C algorithm. Finally, you learned how to implement A3C using Tensorflow and how to handle multiple terminals using TMUX. The tricky part in the implementation is that of the global shared parameters. This is related to creating a cluster of TensorFlow servers. For the readers who want to learn more about this, visit https://www.tensorflow.org/deploy/distributed.

In the following chapters, you will learn more about how to apply reinforcement learning algorithms...