Strengthening Training briefly — it is a paradigm of the educational process, in which the training agent learns over time to behave optimally in a certain environment, continuously interacting in this environment. During the learning process, the agent experiences various situations in the environment in which he finds himself. It is called. The agent, being in this state, can choose from a set of valid actions that can cause different (or penalties). An overtime training agent learns to maximize these rewards in order to behave optimally in any given state.
Q-Learning — is a basic form of Reinforcement Learning that uses Q-Values (also called Action Values) to iteratively improve the behavior of the Learning Agent.
A Time Difference Rule or TD-Update can be represented as follows:
This update rule for estimating the Q value is applied at each time step of the agent`s interaction with the environment. The terms used are explained below. :
-greedy policy — a very simple policy of choosing actions using current Q-value estimates. It looks like this:
Now that the whole theory is in hand, let`s look at an example. We will be using the OpenAI gym to train our Q-Learning model.
Command to install
pip install gym
Before starting with the example, you will need some supporting code to visualize the algorithms. There will be two auxiliary files to download in the working directory. You can find the files here .
Step # 1: Import required libraries.
Step # 2: Create a gym environment.
Step # 3: Make greedy policy.
Step # 4: Build the Q-Learning model.
Step # 5: Train the model.
Step 6: Compile important statistics.
| tr> |
We can see that in the episode reward over time graph, the episode reward gradually increases from em> over time and ultimately flattens out when the episode reward is high, indicating that the agent has learned to maximize the total reward received in the episode through optimal behavior in each episode. state.
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