Introduction

Reinforcement Learning (RL) is a powerful branch of machine learning that focuses on solving sequential decision-making problems. Unlike supervised learning, where an algorithm learns from labeled data, RL agents learn through interaction with an environment, receiving feedback in the form of rewards or penalties.

The RL Framework

At its core, RL is modeled as a feedback control loop between an agent and its environment. The process follows a simple yet profound cycle: the agent observes the current state of the environment, selects an action based on its policy, and then receives a reward and transitions to a new state. This cycle continues until the environment reaches a terminal state or the problem is solved.

Theoretical Foundations

The foundation of RL lies in Markov Decision Processes (MDPs) and optimal control theory. MDPs provide a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision-maker. The Markov property assumes that the next state depends only on the current state and action, not on the history of previous states and actions.

Key Components

Key components of an RL system include:

• States: The set of all possible situations in the environment.
• Actions: The set of all possible moves the agent can make.
• Rewards: The feedback signal that indicates the desirability of the most recent action.
• Policy: A function that maps states to actions, guiding the agent’s behavior.
• Value Functions: Estimates of the expected return from a given state or state-action pair.
• Model: An optional component that predicts the next state and reward given the current state and action.

The Goal of RL

The ultimate goal of an RL agent is to learn a policy that maximizes the cumulative reward over time. This is often expressed as the expectation of returns over many trajectories, where a trajectory is a sequence of state-action-reward tuples in an episode.

Categories of RL Algorithms

RL algorithms can be broadly categorized into three families:

• Value-based methods: These focus on learning value functions to indirectly derive a policy.
• Policy-based methods: These directly optimize the policy without necessarily learning a value function.
• Model-based methods: These learn a model of the environment to plan and make decisions.

Deep Reinforcement Learning

Deep Reinforcement Learning combines RL with deep neural networks, allowing for more complex function approximation. A notable example is the DQN (Deep Q-Network) algorithm, which uses a neural network to approximate Q-values for each action in a given state.

Benfits of RL

• Lack of oracle (no correct answer for each model input)
• Sparsity of feedback
• Data generated during training