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Course Outline

Foundations of Reinforcement Learning

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

Markov Decision Processes (MDPs)

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

Essential RL Algorithms

  • Tabular approaches: Q-Learning and SARSA
  • Policy-based strategies: The REINFORCE algorithm
  • Actor-Critic frameworks and their real-world uses

Deep Reinforcement Learning

  • Getting started with Deep Q-Networks (DQN)
  • The role of experience replay and target networks
  • Policy gradients and advanced deep RL techniques

RL Frameworks and Toolkits

  • Introduction to OpenAI Gym and other RL environments
  • Building RL models using PyTorch or TensorFlow
  • Processes for training, testing, and benchmarking RL agents

Challenges in RL

  • Balancing exploration and exploitation during training
  • Addressing sparse rewards and credit assignment difficulties
  • Managing scalability and computational constraints in RL

Practical Exercises

  • Building Q-Learning and SARSA algorithms from the ground up
  • Training a DQN-based agent to interact with simple games in OpenAI Gym
  • Optimizing RL models for enhanced performance in custom environments

Conclusion and Future Directions

Requirements

  • Solid grasp of machine learning principles and algorithms
  • Proficiency in Python programming
  • Working knowledge of neural networks and deep learning frameworks

Target Audience

  • Machine learning engineers
  • AI specialists
 14 Hours

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