Reinforcement Learning Module

Reinforcement Learning (RL) is an area of machine learning that focuses on how agents should take actions in an environment to maximize some notion of cumulative reward. This module provides an overview of the fundamental concepts and techniques in reinforcement learning.

Key Concepts

• Agent: The decision-making entity in the environment.
• Environment: The surroundings in which the agent operates.
• State: The current situation or configuration of the environment.
• Action: The decision or choice made by the agent.
• Reward: The feedback signal received by the agent after taking an action.

Techniques

• Value-based Methods: These methods learn a value function that estimates the expected return from a given state.
• Policy-based Methods: These methods learn a policy that maps states to actions.
• Model-based Methods: These methods learn a model of the environment and use it to plan actions.

Examples

• Q-learning: A value-based method that learns the optimal Q-values.
• Policy Gradient: A policy-based method that learns the optimal policy by directly optimizing the expected return.
• Deep Q-Network (DQN): A combination of Q-learning and deep learning that allows for the learning of complex policies.

Reinforcement Learning Diagram

For more information on reinforcement learning, you can visit our Reinforcement Learning Tutorial.