Chapter 3. Predicting the best states and actions: Deep Q-networks
This chapter covers
- Implementing the Q function as a neural network
- Building a deep Q-network using PyTorch to play Gridworld
- Counteracting catastrophic forgetting with experience replay
- Improving learning stability with target networks
In this chapter we’ll start off where the deep reinforcement learning revolution began: DeepMind’s deep Q-networks, which learned to play Atari games. We won’t be using Atari games as our testbed quite yet, but we will be building virtually the same system DeepMind did. We’ll use a simple console-based game called Gridworld as our game environment.
Gridworld is actually a family of similar games, but they all generally involve a grid board with ...
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