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Reinforcement Learning

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Reinforcement Learning

ML

Duration
45 Hours

Course Description

               Reinforcement learning is a paradigm that aims to model the trial-and-error learning process that is needed in many problem situations where explicit instructive signals are not available. It has roots in operations research, behavioral psychology and AI.

Course Outline For Reinforcement Learning

1. Introduction to reinforcement learning

  • Defining reinforcement learning (RL) and its applications in different domains.
  • Understanding the differences between supervised, unsupervised, and reinforcement learning.
  • Exploring the basic framework of RL, including agents, environments, actions, states, and rewards.
  • Comparing RL to other AI techniques like dynamic programming and control theory. 

2. Mathematical foundations

  • Markov Decision Processes (MDPs): Understanding MDPs as the mathematical framework for modeling sequential decision-making problems in RL.
  • Bellman Equations: Deriving and solving Bellman equations for optimal value functions and policies.
  • Dynamic Programming: Learning planning algorithms like value iteration and policy iteration.
  • Optimization Techniques: Understanding optimization concepts like gradient descent and its role in RL algorithms. 

3. RL algorithms and methods

  • Multi-Armed Bandits (MAB): Addressing the exploration-exploitation dilemma in simpler settings like multi-armed bandits.
  • Monte Carlo Methods: Learning from experience by averaging sample returns to estimate value functions.
  • Temporal-Difference (TD) Learning: Updating value functions using the Bellman equation and techniques like Q-learning and SARSA.
  • Function Approximation: Using linear function approximation or neural networks to represent value functions in large state spaces.
  • Policy Gradient Methods: Directly optimizing the agent's policy using techniques like REINFORCE and Actor-Critic methods.
  • Deep Reinforcement Learning (DRL): Combining RL with deep learning, including algorithms like Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO). 

4. Advanced topics (depending on course level)

  • Exploration-Exploitation Strategies: Advanced techniques like Upper Confidence Bound (UCB) and UCRL for balancing exploration and exploitation.
  • Offline Reinforcement Learning: Learning from static datasets and real-world interactions without requiring live environmental interactions.
  • Multi-Agent Reinforcement Learning (MARL): Dealing with scenarios involving multiple interacting agents.
  • Hierarchical Reinforcement Learning (HRL): Decomposing complex tasks into subtasks to simplify learning and improve scalability.
  • Reinforcement Learning from Human Feedback (RLHF): Training agents using human feedback and preferences.
  • Direct Preference Optimization (DPO): An alternative to RLHF that optimizes policies based on human preference data without the need for a separate reward model. 

5. Practical application and implementation

  • RL Environments: Working with RL environments like OpenAI Gym for simulating real-world scenarios.
  • Python Programming: Implementing RL algorithms and solutions using Python and relevant libraries (e.g., NumPy, OpenAI Gym).
  • Deep Learning Frameworks: Utilizing frameworks like TensorFlow and PyTorch for building and training DRL models. 
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