Actor Critic Methods Are Easy With Keras

TL;DR

Learn how to code an actor critic agent in the Charis framework and implement custom loss functions for improved performance.

Transcript

what is up everybody in today's tutorial you are gonna code an actor critic agent in the Charis framework as a bonus you're gonna see how you can implement custom loss functions to use loss functions that aren't included in the default Kerris installation let's get started as usual we start with our imports the Charis back-end will give us access t... Read More

Key Insights

• 🧑‍🏭 Actor critic agents consist of an actor network that approximates the policy and a critic network that approximates the value function.
• 🧑‍🏭 Custom loss functions can be implemented in Keras to train the actor network using specific calculations.
• 😆 Actor critic methods are sample inefficient, requiring more iterations compared to deep Q-learning, but can be more straightforward to learn the policy.

Questions & Answers

Q: What is an actor critic agent and how does it differ from deep Q-learning?

An actor critic agent consists of two neural networks: an actor that approximates the policy and a critic that approximates the value function. While deep Q-learning uses a single network to estimate the action-value function, actor critic methods separate the policy and value estimation components.

Q: What is the purpose of custom loss functions in this tutorial?

Custom loss functions are used to train the actor network by calculating the log likelihood of the action taken and the predicted output of the network. By implementing custom loss functions, you can use loss functions that are not included in the default Keras installation.

Q: Why are separate learning rates used for the actor and critic networks?

Unlike deep Q-learning, where weights are copied from one network to another, actor critic methods update both the actor and critic networks independently. Separate learning rates for each network allow them to learn at different rates, which can be beneficial for achieving optimal performance.

Q: How does the agent handle selecting actions and learning from them?

The agent selects actions by feeding observations through the policy network and choosing an action based on the output probabilities. The agent learns from a single state-action-reward-next state transition by calculating target values and updating the actor and critic networks accordingly.

Summary & Key Takeaways

• This tutorial teaches how to code an actor critic agent in the Charis framework and implement custom loss functions.

• The tutorial covers the necessary imports, constructing the deep neural networks, defining custom loss functions, and handling the learning function.

• The code includes a main loop to test and train the agent in the Lunar Lander environment.