Brochure
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- 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
Quick Facts
| particular | details | |||
|---|---|---|---|---|
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 499 ₹799
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Practical Reinforcement Learning - Agents and Environments
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
Articles
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