In this equation, s is the state, a is a set of actions at time t and ai is a specific action from the set. R is the reward table. Q is the state action table but it is constantly updated as we learn more about our system by experience. γ is the learning rate
We will first start with the q-value for the Hold action on July 30.
- The first part is the reward for taking that action. As seen in the R-table it is 0
- Let us assume that γ = 0.98. The maximum Q-value for sell and hold actions on the next day, i.e. 31 July, is 1.09
- Thus q-value for Hold action on 30 July is 0 + 0.98 (1.09) = 1.06
In this way, we will fill the values for the other rows of the Hold column to complete the Q table.
The RL model will now select the hold action to maximise the Q value. This was the intuition behind the Q table. This process of updating the Q table is called Q learning. Of course, we had taken a scenario with limited actions and states. In reality, we have a large state space and thus, building a q-table will be time-consuming as well as a resource constraint.
To overcome this problem, you can use deep neural networks. They are also called Deep Q networks or DQN. The deep Q networks learn the Q table from past experiences and when given state as input, they can provide the Q-value for each of the actions. We can select the action to take with the maximum Q value.
Stay tuned for the next installment to learn how to train artificial neural networks.
Visit QuantInsti to download practical code: https://blog.quantinsti.com/reinforcement-learning-trading/.
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