VTU Module - 5 | Reinforcement Learning

18CS71 | ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING | Module-5 VTU Notes

Reinforcement Learning: An Introduction

Reinforcement Learning (RL) is a dynamic and powerful branch of machine learning that focuses on training intelligent agents to make sequential decisions in an environment to achieve specific goals. Unlike supervised learning, where the model is provided with labeled training data, and unsupervised learning, which deals with unlabeled data, reinforcement learning operates in an environment where the agent learns through trial and error.

The Learning Task in Reinforcement Learning:

The fundamental learning task in reinforcement learning involves an agent interacting with an environment, making decisions over time to maximize a cumulative reward signal. The agent learns by receiving feedback in the form of rewards or punishments based on the actions it takes. The goal is to develop a strategy, or policy, that guides the agent to make optimal decisions to achieve its objectives.

The learning process in RL consists of the following key components:

1. Agent: The intelligent entity responsible for making decisions and interacting with the environment.
2. Environment: The external system with which the agent interacts. It provides feedback to the agent based on its actions.
3. State: The current situation or configuration of the environment, which is essential for decision-making.
4. Action: The decision or move made by the agent at a given state.
5. Reward: The immediate feedback or score received by the agent after taking an action in a particular state. The goal is to accumulate maximum reward over time.
6. Policy: The strategy or set of rules that the agent follows to determine its actions in different states.

Q-Learning:

Q-Learning is a popular and foundational algorithm in reinforcement learning used to train agents in discrete state and action spaces. It falls under the category of model-free learning, meaning it doesn't require knowledge of the underlying dynamics of the environment.

The core idea behind Q-Learning is to estimate the quality of actions in a given state by maintaining a Q-value for each state-action pair. The Q-value represents the expected cumulative reward the agent will receive if it takes a particular action in a specific state and follows the optimal policy thereafter.

The Q-Learning algorithm iteratively updates these Q-values based on the observed rewards, gradually refining the agent's understanding of the optimal strategy. Through this process, the agent learns to make informed decisions that lead to the maximization of long-term rewards, ultimately achieving its objectives in the environment. Q-Learning has been successfully applied in various domains, including robotics, game playing, and autonomous systems.