Deep Q Learning is Simple with Keras | Tutorial

TL;DR

A deep Q-network can be implemented in Keras to tackle the Lunar Lander environment in under 150 lines of code. The tutorial builds a Sequential model and a NumPy-based replay buffer that stores states, actions, rewards, new states, and terminal flags. It also explains circular memory, discrete-action encoding, and correct terminal-state handling. Read on for the implementation details and common mistakes.

Transcript

what's up everybody in this video you are gonna code a deep Q network in Carris and we're gonna beat the lunar lander environment and under 150 lines of code it's gonna be easier than you think and you're gonna see how easy right now so Karis has a number of imports we want to import the dents and activation layers to handle the fully connected as ... Read More

Key Insights

• 😆 The implementation of a deep Q-network in Keras requires importing essential libraries and creating a replay buffer class to handle memory storage.
• 🏛️ The DQN model is built using the sequential object in Keras, with dense layers for fully connected operations and activation layers to apply activation functions.
• ❓ The training process involves choosing actions using an epsilon-greedy approach and learning from state transitions using a temporal difference learning method.
• ⌛ Gradually decreasing epsilon over time helps balance exploration and exploitation in the agent's training.
• 👾 The agent's performance is evaluated using a running average of scores over a specific number of games.

Questions & Answers

Q: How do you implement a deep Q-network in Keras for Lunar Lander?

Build the network with Keras’s Sequential model, using Dense and Activation layers for fully connected operations and activation functions. Create a replay buffer to store state, action, reward, new-state, and done information so the agent can learn from past transitions.

Q: Which imports are needed for the Keras deep Q-network?

Import Dense and Activation from Keras for the network layers, plus Sequential and load_model for building, saving, and loading models. The implementation also uses the Adam optimizer and NumPy for array operations.

Q: What does the replay buffer store?

The replay buffer stores state-action-reward-new-state transition tuples and the environment’s done flags. These saved transitions allow the agent to learn about the problem’s parameter space.

Q: What is the Lunar Lander state input shape?

The Lunar Lander environment provides its state as a vector of eight elements. The replay buffer uses this input shape when allocating its state and new-state memory arrays.

Q: Why does the replay buffer include a discrete flag?

The discrete flag determines how actions are stored and makes the class more extensible. Discrete integer actions are one-hot encoded, while actions for a method such as deep deterministic policy gradients can be stored directly as vectors.

Q: Why is terminal memory stored as 1 minus the done flag?

The terminal value is stored as 1 minus the integer representation of done so it becomes zero when an episode ends. This prevents the Q-target calculation from including a next-state reward that belongs to another episode.

Q: How does the replay buffer overwrite old transitions when it becomes full?

It calculates the next storage index using the memory counter modulo the maximum memory size. After the final array position is reached, the next transition is written at position zero, creating circular storage.

Q: How are discrete actions one-hot encoded in the replay buffer?

The implementation creates a NumPy array of zeros with a length matching the number of actions. It sets the position identified by the selected action to 1.0, then stores that vector in action memory.

Summary & Key Takeaways

• This video focuses on implementing a deep Q-network (DQN) in Keras to train an agent to beat the Lunar Lander environment.

• The code includes importing necessary libraries, creating a replay buffer class to handle memory storage, building the DQN model, and implementing functions for choosing actions and learning from state transitions.

• The agent is trained using a temporal difference learning method, gradually decreasing epsilon to balance exploration and exploitation.

• The performance of the agent is tracked using a running average of scores over 100 games.