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The act of combining Q-learning with a deep neural network is called deep Q-learning, and a deep neural network that approximates a Q-function is called a deep Q-Network, or DQN . Let’s break down how exactly this integration of neural networks and Q-learning works.
Is there such a thing as a deep Q Network?
This is evident from the types of games DQN performs poorly at, near or below human-level [0], in Figure 1. We explore the concept of a deep recurrent Q-network (DRQN), a combination of a recur- rent neural network (RNN) [6] and a deep Q-network (DQN) similar to [5]1.
How are q-values produced in a neural network?
The value produced from a single output node would be the Q-value associated with taking the action that corresponds to that node from the state that was supplied as input to the network. We won’t see the output layer followed by any activation function since we want the raw, non-transformed Q-values from the network.
What kind of input is used to train DQN?
In practice, DQN is trained using an input consisting of the last four game screens. Thus, DQN performs poorly at games that require the agent to remember information more than four screens ago. This is evident from the types of games DQN performs poorly at, near or below human-level [0], in Figure 1.
How to use a neural network for reinforcement learning?
Reinforcement Learning with Neural Networks 1 5.1. Selecting a Neural Network Architecture. 2 5.2. Choosing the Activation Function. 3 5.3. The Loss Function and Optimizer. 4 5.4. Setting up Q-learning with Neural Network. 5 5.5. Performing Q-learning with Neural Network.
How to teach a neural network to play a game?
We code the current position of the player in the input vector by setting the vector to 1 at the player position (step 2.d). Then we check if the memory is full. If it is full we extract a random batch, calculate the update q values and train the network (step 2.e).