Pular para o conteúdo principal
InícioPythonReinforcement Learning with Gymnasium in Python

Reinforcement Learning with Gymnasium in Python

Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.

Comece O Curso Gratuitamente
4 horas15 vídeos52 exercícios2.798 aprendizesTrophyDeclaração de Realização

Crie sua conta gratuita

GoogleLinkedInFacebook

ou

Ao continuar, você aceita nossos Termos de Uso, nossa Política de Privacidade e que seus dados são armazenados nos EUA.
Group

Treinar 2 ou mais pessoas?

Experimente o DataCamp For Business

Amado por alunos de milhares de empresas


Descrição do Curso

Discover the World of Reinforcement Learning

Embark on an exhilarating exploration of Reinforcement Learning (RL), a pivotal branch of machine learning. This interactive course takes you on a comprehensive journey through the core principles of RL where you'll master the art of training intelligent agents, teaching them to make strategic decisions and maximize rewards.

Master Essential Concepts and Tools

Your adventure starts with a deep dive into the unique aspects of RL. You'll not only learn foundational RL concepts but also apply key RL algorithms to practical scenarios using the renowned OpenAI Gym toolkit. This hands-on approach ensures a thorough grasp of RL essentials.

As your journey unfolds, you'll venture into the realms of advanced RL strategies to discover the intricacies of Monte Carlo methods, Temporal Difference Learning, and Q-Learning. By mastering these techniques in Python, you'll be adept at training agents for a variety of complex tasks.

Transform Your Learning into Real-World Impact

Concluding this course, you'll emerge with a profound understanding of RL theory, equipped with the skills to apply it creatively in real-world contexts. You'll be ready to build RL models in Python, unlocking a world of possibilities in your projects and professional endeavors.
Para Empresas

GroupTreinar 2 ou mais pessoas?

Obtenha acesso à biblioteca completa do DataCamp, com relatórios, atribuições, projetos e muito mais centralizados
Experimente O DataCamp for BusinessPara uma solução sob medida , agende uma demonstração.

Nas seguintes faixas

Fundamentos de aprendizado de máquina em Python

Ir para a trilha
  1. 1

    Introduction to Reinforcement Learning

    Gratuito

    Dive into the exciting world of Reinforcement Learning (RL) by exploring its foundational concepts, roles, and applications. Navigate through the RL framework, uncovering the agent-environment interaction. You'll also learn how to use the Gymnasium library to create environments, visualize states, and perform actions, thus gaining a practical foundation in RL concepts and applications.

    Reproduzir Capítulo Agora
    Fundamentals of reinforcement learning
    50 xp
    What is Reinforcement Learning?
    50 xp
    RL vs. other ML sub-domains
    100 xp
    Scenarios for applying RL
    100 xp
    Navigating the RL framework
    50 xp
    RL interaction loop
    100 xp
    Episodic and continuous RL tasks
    100 xp
    Calculating discounted returns for agent strategies
    100 xp
    Interacting with Gymnasium environments
    50 xp
    Setting up a Mountain Car environment
    100 xp
    Visualizing the Mountain Car Environment
    100 xp
    Interacting with the Frozen Lake environment
    100 xp
  2. 2

    Model-Based Learning

    Delve deeper into the world of RL focusing on model-based learning. Unravel the complexities of Markov Decision Processes (MDPs), understanding their essential components. Enhance your skill set by learning about policies and value functions. Gain expertise in policy optimization with policy iteration and value Iteration techniques.

    Reproduzir Capítulo Agora
  3. 3

    Model-Free Learning

    Embark on a journey through the dynamic realm of Model-Free Learning in RL. Get introduced to to the foundational Monte Carlo methods, and apply first-visit and every-visit Monte Carlo prediction algorithms. Transition into the world of Temporal Difference Learning, exploring the SARSA algorithm. Finally, dive into the depths of Q-Learning, and analyze its convergence in challenging environments.

    Reproduzir Capítulo Agora
  4. 4

    Advanced Strategies in Model-Free RL

    Dive into advanced strategies in Model-Free RL, focusing on enhancing decision-making algorithms. Learn about Expected SARSA for more accurate policy updates and Double Q-learning to mitigate overestimation bias. Explore the Exploration-Exploitation Tradeoff, mastering epsilon-greedy and epsilon-decay strategies for optimal action selection. Tackle the Multi-Armed Bandit Problem, applying strategies to solve decision-making challenges under uncertainty.

    Reproduzir Capítulo Agora
Para Empresas

GroupTreinar 2 ou mais pessoas?

Obtenha acesso à biblioteca completa do DataCamp, com relatórios, atribuições, projetos e muito mais centralizados

Nas seguintes faixas

Fundamentos de aprendizado de máquina em Python

Ir para a trilha

colaboradores

Collaborator's avatar
James Chapman
Collaborator's avatar
Chris Harper

pré-requisitos

Supervised Learning with scikit-learnPython ToolboxIntroduction to NumPy
Fouad Trad HeadshotFouad Trad

Machine Learning Engineer

Ver Mais

O que os outros alunos têm a dizer?

Junte-se a mais de 14 milhões de alunos e comece Reinforcement Learning with Gymnasium in Python hoje mesmo!

Crie sua conta gratuita

GoogleLinkedInFacebook

ou

Ao continuar, você aceita nossos Termos de Uso, nossa Política de Privacidade e que seus dados são armazenados nos EUA.