Reinforcement Learning Algorithms 🤖

Reinforcement Learning (RL) is a machine learning paradigm where agents learn to make decisions by interacting with an environment. Here are key algorithms in RL:

1. Q-Learning 🔄

  • Description: A model-free algorithm that learns a policy by estimating Q-values for state-action pairs.
  • Key Feature: Uses a value function to determine optimal actions, even without knowing the environment's dynamics.
  • Explore Q-Learning in depth
Q_Learning

2. Policy Gradients 📈

  • Description: A model-free method that directly optimizes the policy using gradient ascent.
  • Key Feature: Focuses on learning the action-value function rather than the state-value function.
  • Learn about Policy Gradients
Policy_Gradients

3. Deep Q-Networks (DQN) 🕹️

  • Description: Combines Q-Learning with deep neural networks to handle high-dimensional state spaces.
  • Key Feature: Uses experience replay and target networks to stabilize training.
  • See DQN applications
Deep_Q_Networks

4. Actor-Critic Methods 🎭

  • Description: Balances exploration and exploitation by using two networks: actor (policy) and critic (value function).
  • Key Feature: Reduces variance compared to pure policy gradients.
  • Compare Actor-Critic variants
Actor_Critic

For further study, visit our Reinforcement Learning Overview to understand foundational concepts.