Welcome to the Advanced Reinforcement Learning (RL) section! This guide dives deeper into complex concepts and techniques beyond basic RL, ideal for researchers and engineers seeking to master this field.

🔍 Core Concepts

  1. Markov Decision Processes (MDPs)

    • Understand the mathematical framework behind RL environments.
    Markov_Decision_Processes

  2. Policy Gradients

    • Learn how to optimize policies directly using gradient ascent.
    Policy_Gradients

  3. Actor-Critic Methods

    • Combine value function estimation with policy optimization for stability.
    Actor_Critic_Methods

🧰 Advanced Algorithms

  • Q-Learning (with function approximation):
    Q_Learning
  • Deep Q-Networks (DQNs):
    Deep_Q_Networks
  • Policy Improvement via Policy Evaluation (PIPE):
    Policy_Improvement_via_Policy_Evaluation

🌍 Real-World Applications

  • Robotics path planning
  • Game AI (e.g., AlphaGo)
  • Autonomous systems
Real_world_applications

📘 Further Reading

For foundational knowledge, explore our Reinforcement Learning Introduction tutorial. Advanced topics like multi-agent RL and distributional RL are also covered in-depth.

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