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Course Outline

Foundations of Reinforcement Learning

  • An overview of reinforcement learning and its diverse applications
  • Distinguishing between supervised, unsupervised, and reinforcement learning paradigms
  • Core concepts: agents, environments, rewards, and policies

Markov Decision Processes (MDPs)

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

Essential RL Algorithms

  • Tabular approaches: Q-Learning and SARSA
  • Policy-driven methods: The REINFORCE algorithm
  • Actor-Critic frameworks and their specific applications

Deep Reinforcement Learning

  • An introduction to Deep Q-Networks (DQN)
  • Techniques for experience replay and target networks
  • Policy gradients and advanced deep RL methodologies

RL Frameworks and Toolkits

  • Getting acquainted with OpenAI Gym and other RL environments
  • Utilizing PyTorch or TensorFlow for developing RL models
  • Processes for training, testing, and benchmarking RL agents

Challenges in RL

  • Striking a balance between exploration and exploitation during training
  • Managing sparse rewards and credit assignment issues
  • Overcoming scalability and computational hurdles in RL

Practical Exercises

  • Building Q-Learning and SARSA algorithms from the ground up
  • Training a DQN-based agent to play a simple game within OpenAI Gym
  • Refining RL models to enhance performance in custom environments

Conclusion and Future Directions

Requirements

  • A solid command of machine learning principles and core algorithms
  • Advanced proficiency in Python programming
  • Working familiarity with neural networks and deep learning frameworks

Target Audience

  • Machine learning engineers
  • AI specialists
 14 Hours

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  • Format: Online (live), In-company (at your offices), or Hybrid.
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