Intelligent Agents: A Practical Guide to Reinforcement Learning

Introduction

Reinforcement Learning (RL) is a paradigm of machine learning where an agent learns to make optimal decisions by interacting with an environment, receiving rewards for actions that lead to a desired outcome. This trial-and-error process, inspired by behavioral psychology, has enabled a new class of algorithms to solve complex sequential decision-making problems, from training robots to play games to optimizing logistics and financial trading strategies. This course will provide a clear and foundational understanding of RL's core principles and practical applications.

This five-day training is designed to give you the skills to build intelligent agents that can learn and adapt. We will demystify key concepts like states, actions, rewards, and policies, and explore fundamental algorithms such as Q-Learning and Policy Gradients. By combining theoretical knowledge with hands-on coding exercises, you will develop the ability to design, implement, and evaluate RL systems, preparing you to tackle the most challenging problems in AI.

Duration 5 days

Target Audience This course is suitable for data scientists, machine learning engineers, and researchers who have a solid grasp of Python and foundational machine learning concepts, and are interested in building decision-making systems.

Objectives

  1. To understand the fundamental concepts of an RL problem: agent, environment, state, action, and reward.
  2. To grasp the difference between policy-based and value-based RL.
  3. To learn and implement core value-based algorithms like Q-Learning and SARSA.
  4. To understand the trade-off between exploration and exploitation.
  5. To explore how to use neural networks to solve complex RL problems.
  6. To implement basic Policy Gradient methods.
  7. To apply RL to classic control problems and game environments.
  8. To understand key concepts like Markov Decision Processes and Bellman equations.
  9. To evaluate and compare the performance of different RL algorithms.
  10. To work on a final project, applying a learned algorithm to a new problem.

Course Modules

Module 1: The Reinforcement Learning Framework

  • The core components: agent, environment, and state.
  • Understanding actions, rewards, and the reward hypothesis.
  • The concept of a policy and a value function.
  • The difference between a policy and a value function.
  • Simple examples to illustrate the RL loop.

Module 2: Markov Decision Processes (MDPs)

  • Introduction to MDPs and their components.
  • The state transition and reward functions.
  • The Markov property and its importance.
  • Solving small MDPs with value iteration and policy iteration.
  • The Bellman equations and their role in RL.

Module 3: Value-Based Methods

  • The concept of the Q-function.
  • An intuitive explanation of Q-Learning.
  • The SARSA algorithm and its on-policy nature.
  • A step-by-step guide to implementing Q-Learning in Python.
  • The importance of a learning rate and a discount factor.

Module 4: The Exploration-Exploitation Trade-off

  • The core dilemma of RL.
  • Different strategies for balancing exploration and exploitation.
  • Epsilon-greedy and decaying epsilon strategies.
  • Optimistic initialization and its effect.
  • Practical exercises on implementing these strategies.

Module 5: Introduction to Deep Reinforcement Learning

  • The limitations of traditional tabular methods.
  • How neural networks can approximate value functions.
  • An intuitive overview of Deep Q-Networks (DQN).
  • The role of an experience replay buffer.
  • Building a basic DQN with a deep learning framework.

Module 6: Policy-Based Methods

  • The concept of learning a policy directly.
  • A gentle introduction to the Policy Gradient theorem.
  • The REINFORCE algorithm and its implementation.
  • The challenge of high variance in policy gradients.
  • A comparison of policy-based and value-based methods.

Module 7: Advanced Value-Based Methods

  • Double DQN to address overestimation bias.
  • Dueling Networks for more efficient learning.
  • A practical guide to implementing these improvements.
  • The importance of the target network in DQNs.
  • Case studies on applying advanced value-based methods.

Module 8: The Actor-Critic Architecture

  • The intuition behind the Actor-Critic model.
  • The role of the Actor (policy) and the Critic (value function).
  • The advantage of combining both value and policy methods.
  • A simple implementation of an Actor-Critic agent.
  • An overview of the A3C and A2C algorithms.

Module 9: Classic Control Problems

  • Solving the CartPole problem.
  • Implementing an agent for the Mountain Car problem.
  • Using RL to solve a game like Frozen Lake.
  • The importance of state and action spaces.
  • A hands-on project with a classic control problem.

Module 10: Reinforcement Learning in Practice

  • Challenges in applying RL to real-world problems.
  • A deep dive into a real-world case study.
  • The importance of reward shaping.
  • The ethical considerations of using RL in production.
  • A discussion on the future of reinforcement learning.

Module 11: Multi-Agent Reinforcement Learning

  • The challenges of having multiple agents.
  • Cooperative vs. competitive environments.
  • Simple examples of multi-agent systems.
  • An overview of advanced multi-agent algorithms.
  • Practical applications in robotics and autonomous systems.

Module 12: Advanced Topics and Libraries

  • An introduction to model-based RL.
  • Offline vs. Online RL.
  • An overview of popular RL libraries like Stable Baselines.
  • The role of Gym and other simulation environments.
  • Using different neural network architectures for RL.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport Pick Up is provided by the institute. Accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Intelligent Agents: A Practical Guide To Reinforcement Learning in Namibia
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