Reinforcement Learning

➺ Core Components

  • State space
  • Action space
  • Reward function
  • Policy optimization 

    • ➺ Technical Framework:

    Algorithms
  • Q-Learning
  • Deep Q Networks (DQN)
  • Policy Gradient Methods
  • Actor-Critic Models

    • ➺ Learning Approaches:

  • Value-based methods
  • Policy-based methods
  • Model-based methods
  • Hybrid approaches 

    • ➺ Advanced Concepts:

    Training Strategies 
  • Experience replay
  • Exploration vs exploitation
  • Multi-agent systems
  • Transfer learning 

    • ➺ Optimization Techniques:

  • Policy optimization
  • Value function approximation
  • Model-based planning
  • Hierarchical learning