Actor Critic Methods Are Easy With Keras

Today you’ll see how to code an Actor Critic Deep Reinforcement Learning Agent in the Keras Framework. You’ll also get to see how we can implement custom loss functions in Keras, which isn’t something I’ve seen widely talked about.

Actor Critic Methods are a type of temporal difference policy gradient algorithm that is somewhat sample inefficient, yet highly effective due to the fact that the policy is often a simpler function to approximate than the action-value function.

It works by having the actor approximate the agent’s policy (what the agent uses to choose actions), while the critic network approximates the value of those actions. The critic informs the agent of the value of those actions and the actor updates its neural network to tilt the policy’s probability distribution in the direction of the actions with the highest value.

#Keras #ActorCritic #DeepReinforcementLearning

Learn how to turn deep reinforcement learning papers into code:

Deep Q Learning:

Actor Critic Methods:

Curiosity Driven Deep Reinforcement Learning

Natural Language Processing from First Principles: Learning Fundamentals

Here are some books / courses I recommend (affiliate links):
Grokking Deep Learning in Motion:
Grokking Deep Learning:
Grokking Deep Reinforcement Learning:

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