This module introduces the foundational concepts of reinforcement learning, including the roles of agents, environments, and the flow of information through rewards and observations. Learners will explore Markov processes and how they evolve into Markov decision processes by incorporating actions and rewards. By the end, you'll understand the basic structure and challenges of designing reinforcement learning systems.

This module introduces learners to the Gymnasium library and the OpenAI Gym API, essential tools for building and interacting with reinforcement learning environments in Python. You will explore environment structure, naming conventions, and how to create and use environments programmatically. Practical examples, including implementing a simple agent, will help solidify your understanding of these foundational RL tools.

This module introduces the foundational concepts and practical tools for building deep learning models using PyTorch. Learners will explore tensor operations, automatic gradient computation, neural network components, loss functions, and experiment monitoring with TensorBoard and Ignite. By the end, you'll be equipped to construct, train, and evaluate neural networks efficiently.

This module introduces the cross-entropy method as a reinforcement learning technique, guiding learners through its implementation and application to classic environments like CartPole and FrozenLake. Learners will gain practical experience building and tuning neural network models to solve RL tasks using this approach.

This module introduces foundational tabular reinforcement learning methods, focusing on the Bellman equation and its role in value-based algorithms. Learners will explore value and Q-functions, and implement value iteration and Q-iteration techniques using practical examples like FrozenLake.

This module introduces the principles and implementation of Deep Q-Networks (DQNs), covering foundational concepts such as the Bellman equation, value iteration, and tabular Q-learning. Learners will explore how neural networks can approximate Q-values in complex environments, optimize training using stochastic gradient descent, and evaluate DQN performance on challenging tasks like Atari Pong. By the end, students will understand both the theory and practical aspects of training deep reinforcement learning agents.

This module introduces key abstractions and tools for implementing deep reinforcement learning agents using higher-level libraries. Learners will explore agent architectures, policy distributions, experience sources, and replay buffers, gaining practical skills to build and train DQN-based models efficiently.