Python Reinforcement Learning, Deep Q-Learning and TRFL

BY
Udemy

Mode

Online

Fees

₹ 499 799

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course and certificate fees

Fees information
₹ 499  ₹799
certificate availability

Yes

certificate providing authority

Udemy

The syllabus

Practical Reinforcement Learning - Agents and Environments

  • The Course Overview
  • Install RStudio
  • Install Python
  • Launch Jupyter Notebook
  • Learning Type Distinctions
  • Get Started with Reinforcement Learning
  • Real-world Reinforcement Learning Examples
  • Key Terms in Reinforcement Learning
  • OpenAI Gym
  • Monte Carlo Method
  • Monte Carlo Method in Python
  • Monte Carlo Method in R
  • Practical Reinforcement Learning in OpenAI Gym
  • Markov Decision Process Concepts
  • Python MDP Toolbox
  • Value and Policy Iteration in Python
  • MDP Toolbox in R
  • Value Iteration and Policy Iteration in R
  • Temporal Difference Learning
  • Temporal Difference Learning in Python
  • Temporal Difference Learning in R
  • Test Your Knowledge

Advanced Practical Reinforcement Learning

  • The Course Overview
  • Introduction to Deep Reinforcement Learning
  • Deep Q-Learning and Double Deep Q-Learning
  • Q-Learning in Python
  • Q-Learning in R
  • TensorFlow
  • TensorFlow in Python
  • Deep Q-Learning with TensorFlow in Python
  • Keras
  • Keras in Python
  • Deep Q-Learning with Keras in Python
  • Deep Q-Learning with Keras in R
  • Case Study – Reinforcement Learning
  • Test Your Knowledge

Hands-On Deep Q-Learning

  • The Course Overview
  • Artificial Intelligence in a Nutshell
  • Reinforcement Learning Dynamics
  • The Bellman Equation
  • Markov Decision Process
  • Policy versus Plan and Living Penalty
  • Q-Learning Intuition
  • Temporal Difference
  • Learning Phase of Deep Q-Learning
  • Acting Phase of Deep Q-Learning
  • Experience Reply and Action Selection Policies
  • Installing PYTORCH environment
  • Self Driving Car – Part 1
  • Self Driving Car – Part 2
  • Self Driving Car – Part 3
  • Playing with Our SDC AI
  • Convolutional Neural Network
  • Deep Convolutional Q-Learning
  • Eligibility Trace
  • Installing OpenAIGym and ppaquette
  • Build an AI for DOOM – Part 1
  • Build an AI for DOOM – Part 2
  • Build an AI for DOOM – Part 3
  • Playing with our AI in DOOM
  • Test Your Knowledge

Reinforcement Learning with TensorFlow & TRFL

  • The Course Overview
  • Set Up and Installation
  • Getting Started with TD Learning
  • Exploiting Off-policy Efficiency Using Q Learning
  • Comparing On-policy Methods with SARSA and SARSE
  • Implementing a Deep Q Network and Applying Target Network Updates
  • Modifying a DQN with Double DQN, Persistent DQN, and Huber Loss
  • Improving a DQN with Distributional Q Learning
  • Utilizing Policy Gradient Methods
  • Increasing Exploration with Policy Entropy Loss
  • Applying Actor-Critic with A3C and A2C
  • Performing Deterministic Policy Gradients
  • Deploying TD(λ)
  • Balancing Bias and Variance with Generalized λ Returns
  • Applying Q(λ)
  • Working with Multi-step Forward View
  • Using Importance Sampling with Retrace (λ)
  • Getting Started with Impala with V-Trace
  • Augmenting an Agent with Unreal and Pixel Control
  • Test Your Knowledge

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