Sampling From a Stochastic Policy

Q-Table Initialization and Indexing

States, Actions, and Rewards Defined

Policy: Mapping States to Actions

States, Actions, and Rewards Defined

Agent, Environment, and Interaction Loop

Real-World Applications of RL

Reward Shaping and Reward Design

The Credit Assignment Problem

Episodic vs Continuing Tasks

On-Policy vs Off-Policy Learning

Temporal Difference Learning Concept

Q-Learning as a Basic RL Algorithm

Model-Based vs Model-Free RL

Markov Decision Process: The RL Framework

Epsilon-Greedy Action Selection

Exploration vs Exploitation Tradeoff

Value Functions: State and Action (Q)

Discount Factor in Reinforcement Learning

Reward Signal vs Long-Term Return

Monte Carlo Return Estimation

Environment Step Loop

TD(0) Value Update

Reward vs Return Distinction

Terminal State Handling

TD Error Computation

Learning Rate Effect in Updates

Discounted Return Sum

One-Step Q Value

Bellman Optimality Backup

Value Iteration Init

Bellman Expectation Backup

Gamma Value Range

MDP Step Function

Estimating State Value

SARSA Update Rule

Q-Learning Update Rule

Exploration vs Exploitation Value

Greedy Policy Extraction

Epsilon-Greedy Selection

On-Policy vs Off-Policy

Policy Evaluation Bug

Epsilon Decay Schedule

Deep Reinforcement Learning Overview

Real-World Applications of RL

RL vs Supervised and Unsupervised Learning

Reward Signal

Value-Based vs Policy-Based Methods

States and Observations

Credit Assignment Problem

RL vs Supervised and Unsupervised

Model-Based vs Model-Free RL

Temporal Difference Learning

Discount Factor Gamma

Actions and Action Space

Q-Learning Basics

Agent-Environment Interaction Loop

Real-World RL Applications

Policy Definition

Episodes and Terminal States

Markov Decision Process

Q-Value Action-Value Function

Return and Cumulative Reward

Exploration vs Exploitation

Deep Reinforcement Learning Overview

Reward Shaping and Sparse Rewards

What Reinforcement Learning Is

Epsilon-Greedy Strategy

Value Function

On-Policy vs Off-Policy

Reward Shaping and Sparse Rewards

Epsilon-Greedy Strategy

Q-Learning Algorithm

Monte Carlo Methods in RL

Temporal Difference (TD) Learning

Policy-Based vs Value-Based Methods

SARSA Algorithm

Credit Assignment Problem

On-Policy vs Off-Policy Learning

Bellman Equation

Episodic vs Continuing Tasks

Model-Based vs Model-Free RL

Markov Property

Return and Cumulative Reward

Rewards and Reward Signal

Transition Dynamics / Environment Model

Markov Decision Process (MDP)

Action-Value Function (Q-Function)

Agent-Environment Interaction Loop

Value Function (State-Value)

Actions and Action Space

Discount Factor (Gamma)

Policy: Deterministic vs Stochastic

States and State Space