Suppose that states for a certain environment are represented as three$-$element tuples of floating point values. Assume also that agents in the environment has three actions Final Layer Weights

Suppose that a state $s$ is provided to the network. After the second layer is processed, the output of the nodes A$,$ B$,$ C$,$ and D$,$ are as shown below:

$A$: $3\mathrm{.}4,B$:$6\mathrm{.}2,C$:$0\mathrm{.}0,D$:$2\mathrm{.}6$

Use the information provided to calculate $Q_{}(s,1).$

available from any state, and that the actions are encoded as integers $0,1,$ and $2.$ The neural network used to approximate the action$-$value function in a DQN for the

environment is shown in the figure below. A description of the network is as follows:

The three values representing a state provided as inputs to the network.

The network has $2$ hidden layers, each with $4$ nodes, and each using a ReLU activation function.

The output layer has three nodes, one for each action.

The final layer does not use an activation function, and simply outputs the weighted sums of the inputs.

Notice that in the diagram above, the input nodes for the final layer are denoted by letters A $-$ D$,$ and the output nodes are denoted by letters E $-$ G$.$ The weight associated with

each connection between pairs of nodes in the final layer are provided in the table below.