Get in Touch

Course Outline

Introduction to Reinforcement Learning

  • Overview of reinforcement learning and its various applications.
  • Distinguishing between supervised, unsupervised, and reinforcement learning.
  • Key concepts: agents, environments, rewards, and policies.

Markov Decision Processes (MDPs)

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

Core RL Algorithms

  • Tabular methods: Q-Learning and SARSA.
  • Policy-based methods: The REINFORCE algorithm.
  • Actor-Critic frameworks and their practical uses.

Deep Reinforcement Learning

  • Introduction to Deep Q-Networks (DQN).
  • Experience replay and target networks.
  • Policy gradients and advanced deep RL methods.

RL Frameworks and Tools

  • Familiarization with OpenAI Gym and other RL environments.
  • Utilizing PyTorch or TensorFlow for developing RL models.
  • Processes for training, testing, and benchmarking RL agents.

Challenges in RL

  • Balancing exploration and exploitation during training.
  • Managing sparse rewards and credit assignment problems.
  • Addressing scalability and computational hurdles in RL.

Hands-On Activities

  • Implementing Q-Learning and SARSA algorithms from scratch.
  • Training a DQN-based agent to play simple games in OpenAI Gym.
  • Fine-tuning RL models to enhance performance in custom environments.

Summary and Next Steps

Requirements

  • A solid grasp of machine learning principles and algorithms.
  • Proficiency in Python programming.
  • Knowledge of neural networks and deep learning frameworks.

Target Audience

  • Machine learning engineers.
  • AI specialists.
 14 Hours

Number of participants


Price per participant

Upcoming Courses

Related Categories