Deep Q Networks Explained: Teaching a Robot to Collect Rocks
Imagine you want to build a rock-collecting robot, you would need a metal body, wheels for movement, a battery, a microcontroller, sensors, a servo motor, actuators, and programming language to code it.
Now, working on the hardware part right is quite challenging but it is also challenging to program a robot in such a way that we don't need to instruct and monitor it every time it finds a rock.
To solve this challenge in 2015 Deep Mind introduced the Deep Q network a type of reinforcement learning architecture that allows you to train an agent capable of learning to excel at a diverse array of challenging tasks.
Let’s get back to our rock-collecting robot!!
We want to build a robot that can identify different types of rocks in a field. The robot learns by trial and error, trying to figure out which rocks are valuable and which are not. Here’s how we can use the analogy to explain Deep Q-Networks (DQN):
How does DQN work?
To learn anything new one must start the process our robot does that too it gets initialized with random ideas of what it is supposed to do. Mind you, it doesn't know fully yet what the result would look like.
In the Deep Q Network neural network gets initialized by random weights, we do so to ensure that our model has more options to make optimal decisions.
The Exploration of the Environment:
The environment is the field where your robot operates. It contains various rocks that need to be identified. When the robot moves around and examines a rock, it observes certain features (like size, colour, texture) and tries to determine whether it’s a valuable rock or not.
Action Selection:
As the robot explores the environment it gets some idea of what action it should take next. In DQN this happens via a Q-eval Network, it is like the robot’s brain, which takes the observed features (input layer) and processes them through several hidden layers. Based on this processing, it outputs a “guess” or prediction about the value of the rock.
Environment Interaction
Our robot is now scurrying through a vast field of rocks some are collectibles others not so. In this stage, our agent interacts with the environment and updates its Q-values (Q-values are numerical values representing the expected future rewards an agent can obtain by taking a specific action in a given state. They measure the "quality" of an action in that state.)
Experience Replay
Our robot can remember past interactions, it knows which rocks were valuable and which were not. This memory is supported by the Target Network in DQN.
The target network represents the robot’s “mentor,” a more stable and less frequently updated brain that guides the robot. While the Q-eval network makes guesses based on the robot’s current knowledge, the target network provides a benchmark or target to aim for. The target network is updated less frequently, allowing the robot to stabilize its learning.
Summary:
- Experience Collection: The robot goes into the field (the environment) and starts examining rocks. Each time it encounters a rock, it uses its Q-eval network to decide whether the rock is valuable or not. It then gets feedback from the environment (like discovering whether the rock is indeed valuable) and records this in its replay memory.
- Learning from Experiences: The robot randomly picks past experiences from replay memory and uses them to train its Q-eval network. It compares its current “guess” with what the target network would have predicted, and adjusts its understanding (updates the network) to minimize the difference.
- Updating the Mentor: After some time, the robot’s mentor (target network) is updated to reflect the robot’s improved understanding. This helps the robot have a stable guide while still learning from new experiences.
- DQN Loss Function: The DQN loss function is like the robot’s tutor, which tells it how well it is learning. It compares the robot’s guesses with what the mentor says and provides a signal (gradient) that helps the robot improve its guesses over time.
Thanks for reading if you want to learn more about Q-learning and DQN head to this article.
Authors Note: If you liked this post, please clap, share and follow me for more AI with an analogy.
