Lecture 1: Introduction to Reinforcement Learning | Highlights and Annotations by Gistr.

Reinforcement learning (RL) trains agents to maximize cumulative rewards via trial-and-error interaction with an environment. Unlike supervised learning, it lacks explicit instruction; unlike unsupervised learning, it has a clear objective (reward maximization). Key concepts include Markov states, policies, value functions, and the exploration-exploitation dilemma. RL agents can be model-based or model-free, value-based or policy-based. This segment effectively positions reinforcement learning within a broader scientific context, highlighting its intersection with various fields like computer science, engineering, neuroscience, psychology, mathematics, and economics. It emphasizes the fundamental nature of reinforcement learning as the science of decision-making. This segment provides a concise overview of the reinforcement learning course, including its structure, assessment details (coursework and exam components), and recommended textbooks. It also clarifies the course's flexible structure, allowing students to focus on either reinforcement learning or kernel methods. This segment clearly differentiates reinforcement learning from supervised and unsupervised learning, emphasizing key distinctions such as the absence of a supervisor, delayed feedback, the importance of time, and the active learning aspect where the agent influences its environment.This segment presents diverse and engaging real-world examples of reinforcement learning problems, ranging from controlling a helicopter to managing an investment portfolio, controlling a power station, making a humanoid robot walk, and playing Atari games. This illustrates the breadth of applications and the practical impact of reinforcement learning. This segment showcases the AI's performance across various Atari games, highlighting its ability to learn human-like strategies without explicit programming. The AI demonstrates adaptive gameplay, adjusting its approach based on game mechanics and increasing speed, showcasing the power of reinforcement learning. This segment illustrates the interaction between agents and environments in reinforcement learning. It explains the trial-and-error loop, where the agent receives observations and rewards, takes actions, and the environment responds accordingly. The discussion emphasizes the importance of building an algorithm that maps the agent's history to its actions. This segment provides concrete examples of reward signals in various real-world applications, including helicopter maneuvers, board games, financial portfolios, power station management, and robot locomotion. It emphasizes the unifying framework of reinforcement learning, showing how diverse problems can be addressed using the same formalism. This segment delves into the core concept of rewards in reinforcement learning, explaining how they are used to define goals and how the system handles scenarios with no intermediate rewards. The discussion explores the controversial hypothesis that all goals can be described by maximizing expected cumulative reward, sparking thought on the limitations and implications of this approach. This segment provides a more formal definition of state, introducing the concept of the Markov state (or information state). The speaker explains the Markov property, which states that the future is independent of the past given the present state. This implies that a Markov state contains all the necessary information for predicting the future, allowing us to discard past history. The discussion includes examples to illustrate the concept, such as a helicopter's state being defined by its current position, velocity, and other relevant factors. This segment introduces the concepts of environment state and agent state in reinforcement learning. The speaker explains that the environment state is the internal information used by the environment to determine what happens next, while the agent state is the information the algorithm uses to make decisions. The distinction is crucial because the agent doesn't always have access to the environment state. The discussion also touches upon multi-agent systems and how they can be incorporated into this framework. This segment clearly defines the three main components of a reinforcement learning agent: policy (how the agent selects actions), value function (predicting the goodness of states and actions), and model (the agent's understanding of the environment). It establishes these as key concepts for understanding RL, highlighting their individual roles and explaining how they might or might not be included in an agent's design. This segment uses a simple example of a rat learning through trial and error to illustrate different ways to represent agent state. The speaker presents scenarios where the rat receives either cheese or an electric shock based on its actions. The example demonstrates how different state representations (e.g., the last three events, counts of events) lead to different predictions about future outcomes, highlighting the importance of choosing a useful state representation.This segment distinguishes between fully observable and partially observable environments. In fully observable environments, the agent has access to the complete environment state, leading to the Markov Decision Process (MDP) framework. However, in partially observable environments, the agent only observes parts of the environment, necessitating the creation of an agent state distinct from the environment state. The speaker discusses various approaches for handling partial observability, including remembering the entire history, building probabilistic beliefs, and using recurrent neural networks. This segment uses visualizations from the Atari and Space Invaders games to illustrate the concept of the value function. It shows how the value function, a prediction of future reward, oscillates based on the agent's proximity to scoring opportunities. The discussion includes the impact of reward time scales and how the value function inherently accounts for risk in maximizing reward.This segment introduces the concept of models in RL agents, explaining that a model is not the environment itself but rather the agent's representation of how the environment works. It breaks down models into transition models (predicting next states) and reward models (predicting rewards), illustrating their use in planning and decision-making. The optional nature of models and the existence of model-free methods are also clarified. This segment clearly distinguishes between reinforcement learning (environment unknown, learning through interaction) and planning (environment known, internal computation using a model). It uses the example of a robot in a factory and a helicopter navigating wind to illustrate the core difference between these two approaches to sequential decision-making. This segment presents a taxonomy for categorizing RL agents based on the presence or absence of the three core components (policy, value function, model). It introduces value-based, policy-based, and actor-critic agents, explaining the distinctions between them and clarifying the fundamental difference between model-free and model-based approaches to reinforcement learning. This segment elaborates on the crucial trade-off between exploration (discovering new information about the environment) and exploitation (using existing knowledge to maximize reward). It uses relatable examples like choosing restaurants and online advertising to illustrate the concept and its importance in achieving optimal performance. This segment differentiates between prediction (evaluating the performance of a given policy) and control (finding the optimal policy). It uses a grid-world example to visually demonstrate how the value function differs significantly between a random policy and an optimal policy, highlighting the importance of solving the prediction problem to effectively address the control problem.