Skip to content

Latest commit

 

History

History
19 lines (14 loc) · 2.63 KB

Part V: Advanced Strategies and Optimization.md

File metadata and controls

19 lines (14 loc) · 2.63 KB

Part V: Advanced Strategies and Optimization

Chapter 12: Genetic Algorithms for Optimization

  • Introduction to Genetic Algorithms (GA): Provides an overview of genetic algorithms, a type of optimization algorithm inspired by the principles of natural selection and genetics.
  • Genetic Representation: Discusses how solutions are represented as chromosomes or individuals, composed of genes that encode candidate solutions to optimization problems.
  • Selection Operators: Explores selection mechanisms such as roulette wheel selection, tournament selection, and rank-based selection, which determine the individuals chosen for reproduction.
  • Crossover and Mutation: Details crossover and mutation operators used to generate new offspring from parent individuals, facilitating exploration of the solution space and maintaining diversity.
  • Fitness Evaluation: Describes the fitness function used to evaluate the quality of candidate solutions and guide the evolutionary process toward optimal or near-optimal solutions.

Chapter 13: Reinforcement Learning Techniques

  • Overview of Reinforcement Learning (RL): Introduces reinforcement learning as a machine learning paradigm where an agent learns to interact with an environment to maximize cumulative rewards over time.
  • Value-Based Methods: Discusses value-based methods such as Q-learning and Deep Q-Networks (DQN), which learn value functions to estimate the expected return of taking actions in a given state.
  • Policy-Based Methods: Explores policy-based methods like policy gradients and actor-critic architectures, which directly parameterize the agent's policy to maximize expected rewards.
  • Model-Based Methods: Describes model-based approaches that learn a model of the environment dynamics and use it for planning and decision-making.
  • Deep Reinforcement Learning: Discusses the integration of deep neural networks with reinforcement learning, enabling agents to learn complex policies and value functions from high-dimensional sensory inputs.

In Part V, readers delve into advanced optimization strategies such as genetic algorithms and cutting-edge reinforcement learning techniques, providing them with a comprehensive understanding of state-of-the-art methods for training autonomous racing agents in AWS DeepRacer.