Corporate Reinforcement Training Course

An introduction to Reinforcement Learning (RL) and its applications. It covers the basic principles of RL, its relevance in decision-making processes, and how it differs from other machine learning techniques.

This module introduces the technical terms and concepts used in RL, such as state, action, reward, and policy. It also explains how these terms relate to each other and contribute to the overall RL algorithm.

Explains the fundamental features of RL, including the agent-environment interaction, the reward signal, and the goal-directed nature of RL.

Discusses the core components of RL, such as the agent, the environment, the state, the action, and the reward. It also explains how these elements work together to achieve the RL objective.

The different approaches to implementing RL, include model-based and model-free methods. It also discusses the advantages and disadvantages of each approach.

Provides an in-depth understanding of how RL works, including the agent's decision-making process, the exploration-exploitation tradeoff, and the learning process.

Explains the Bellman equation, which is a central concept in RL. It discusses how the Bellman equation is used to compute the value of a state and the importance of the Bellman optimality equation in determining the optimal policy.

Covers the different types of RL, such as value-based, policy-based, and actor-critic methods. It also discusses the advantages and disadvantages of each type.

An overview of the RL algorithm, including the value iteration and policy iteration methods. It also explains how RL algorithms are optimized to achieve faster convergence and better performance.

Introduces the Markov Decision Process (MDP), which is the mathematical framework used to model RL problems. It covers the key components of an MDP, such as the state space, action space, transition function, and reward function.

An in-depth explanation of Q-Learning, which is a popular RL algorithm used to learn the optimal policy. It covers the Q-Learning algorithm, the Q-Table, and the exploration-exploitation tradeoff.

Discusses the key differences between Supervised Learning (SL) and RL. It covers the objective, data, feedback, and algorithmic differences between the two machine learning techniques.

Covers the various applications of RL, such as game playing, robotics, and autonomous driving. It also discusses the challenges and limitations of RL in real-world applications.