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and for value estimation. Deep Q Learning --------------- First, there’s the following problem with Q learning. Let’s suppose we have a simple world state, say {standing, walking}, and a goal state, {walking, sitting}. To move from standing to sitting we need to first be in the walking state, then in the sitting state. However, our model doesn’t know that. It instead tries to find a policy that moves us from one state to another, e.g. moving from standing to sitting. While this is technically correct, it doesn’t quite work. Q Learning solves this by not choosing the action at all. Instead, it uses the previous action to estimate the current value of the next state. In our running example, if we choose the walking action, the value we’d estimate for the next state would be 0, as we haven’t moved from standing to walking. If we then chose the sitting action, the value we’d estimate for the next state would be –1, as we haven’t moved from walking to sitting, we’ve just moved from standing to sitting. The point is that the Q network doesn’t learn a policy to move from state to state; it learns the value of each state. This works fine for simple problems. However, for games, it’s a pain. For a game, like Tenacity, which has hundreds of states, that means tens of thousands of parameters that need to be trained. To get around this, a single network can be used for the both policy and value networks. The policy network estimates the actions, which can then be used to estimate the value of states. Double Deep Q Learning ---------------------- Unfortunately, using the same network for both policy and value estimation means that the networks aren’t really communicating. Double Q Learning improves this by introducing two separate networks, one for policy and one for value. The policy network is used to choose the best action. The value network is used to estimate the value of each state. The key is that the policy network doesn’t need to know anything about the value network. Instead, the policy network learns to predict a value for each state, and then picks the action that maximizes that value. Using the same network for policy and value estimation also removes the need to choose between the learning methods. As a

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