How to train artificial neural networks?
We will use the concept of experience replay. You can store the past experiences of the agent in a replay buffer or replay memory. In layman language, this will store the state, action taken and reward received from it. And use this combination to train the neural network.
There are mainly two issues which you have to consider while building the RL model. They are as follows:
Type 2 Chaos
This might feel like a science fiction concept but it is very real. While we are training the RL model, we are working in isolation. Here, the RL model is not interacting with the market. But once it is deployed, we don’t know how it will affect the market.
Type 2 chaos is essentially when the observer of a situation has the ability to influence the situation. This effect is difficult to quantify while training the RL model itself. However, it can be reasonably assumed that the RL model is still learning even when it is deployed and thus will be able to correct itself accordingly.
Noise in Financial Data
There are situations where the RL model could pick up random noise which is usually present in financial data and consider it as input which should be acted upon. This could lead to inaccurate trading signals.
While there are ways to remove noise, we have to be careful of the tradeoff between removing noise and losing important data.
While these issues are definitely not something to be ignored, there are various solutions available to reduce them and create a better RL model in trading.
We have only touched the surface of reinforcement learning with the introduction of the components which make up the reinforcement learning system. The next step would be to take this learning forward by implementing your own RL system to backtest and paper trade on real-world market data.
Visit QuantInsti to download practical code and to learn more about their Deep reinforcement learning educational materials: https://blog.quantinsti.com/reinforcement-learning-trading/.
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