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Course Outline

Introduction to Reinforcement Learning

  • Overview of reinforcement learning and its various applications
  • Distinguishing between supervised, unsupervised, and reinforcement learning
  • Key concepts: agent, environment, rewards, and policy

Markov Decision Processes (MDPs)

  • Understanding states, actions, rewards, and state transitions
  • Value functions and the Bellman Equation
  • Using dynamic programming to solve MDPs

Core RL Algorithms

  • Tabular methods: Q-Learning and SARSA
  • Policy-based methods: REINFORCE algorithm
  • Actor-Critic frameworks and their uses

Deep Reinforcement Learning

  • Introduction to Deep Q-Networks (DQN)
  • Experience replay and target networks
  • Policy gradients and advanced deep RL methods

RL Frameworks and Tools

  • Introduction to OpenAI Gym and other RL environments
  • Using PyTorch or TensorFlow for developing RL models
  • Training, testing, and benchmarking RL agents

Challenges in RL

  • Balancing exploration and exploitation during training
  • Addressing sparse rewards and credit assignment issues
  • Scalability and computational difficulties in RL

Practical Activities

  • Implementing Q-Learning and SARSA algorithms from the ground up
  • Training a DQN-based agent to play a simple game in OpenAI Gym
  • Optimizing RL models for better performance in custom environments

Summary and Next Steps

Requirements

  • A solid understanding of machine learning principles and algorithms
  • Proficiency in Python programming
  • Familiarity with neural networks and deep learning frameworks

Target Audience

  • Machine learning engineers
  • AI specialists
 14 Hours

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