Introduction to Reinforcement Learning
Chapter 1: Foundations of Reinforcement Learning
What is Reinforcement Learning?
Agents and Environments
States, Actions, and Rewards
Policies: Mapping States to Actions
The RL Workflow: Interaction Loops
Types of RL Tasks: Episodic vs Continuing
Comparing RL with Other Learning Types
Setting up Your Python Environment for RL
Quiz for Chapter 1
Chapter 2: Markov Decision Processes (MDPs)
Modeling Sequential Decision Making
Formal Definition of an MDP
State Transition Probabilities
Reward Functions
Return: Cumulative Future Rewards
Discounting Future Rewards
Policies and Value Functions (Vπ, Qπ)
Finding Optimal Policies
Quiz for Chapter 2
Chapter 3: Estimating Value Functions
The Bellman Expectation Equation
The Bellman Optimality Equation
Solving Bellman Equations (Overview)
Dynamic Programming: Policy Iteration
Dynamic Programming: Value Iteration
Limitations of Dynamic Programming
Quiz for Chapter 3
Chapter 4: Monte Carlo Methods
Learning from Complete Episodes
Monte Carlo Prediction: Estimating Vπ
Monte Carlo Control: Estimating Qπ
On-Policy vs Off-Policy Learning
MC Control without Exploring Starts
On-Policy First-Visit MC Control Implementation
Off-Policy MC Prediction and Control Intro
Practice: Implementing MC Prediction
Quiz for Chapter 4
Chapter 5: Temporal-Difference Learning
Learning from Incomplete Episodes
TD(0) Prediction: Estimating Vπ
Advantages of TD Learning over MC
SARSA: On-Policy TD Control
Q-Learning: Off-Policy TD Control
Comparing SARSA and Q-Learning
Expected SARSA
Hands-on Practical: Implementing Q-Learning
Quiz for Chapter 5
Chapter 6: Function Approximation in RL
Handling Large State Spaces
Value Function Approximation (VFA)
Feature Vectors for State Representation
Linear Methods for VFA
Gradient Descent for Parameter Learning
Semi-gradient TD Methods
Using Neural Networks for VFA
Practice: Applying Linear VFA
Quiz for Chapter 6
Chapter 7: Introduction to Deep Q-Networks (DQN)
Combining Q-Learning with Deep Learning
Challenges with Neural Networks in RL
Experience Replay Mechanism
Fixed Q-Targets (Target Networks)
The DQN Algorithm Structure
Architectural Considerations for DQNs
Hands-on Practical: Building a Basic DQN
Quiz for Chapter 7
Chapter 8: Introduction to Policy Gradient Methods
Learning Policies Directly
Policy Gradient Theorem (Concept)
REINFORCE Algorithm
Baselines for Variance Reduction
Actor-Critic Methods Overview
Comparing Value-Based and Policy-Based Methods
Practice: Implementing REINFORCE
Quiz for Chapter 8
Baselines for Variance Reduction
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- Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, 2018 (MIT Press) - A standard textbook for reinforcement learning, offering a thorough explanation of policy gradient methods, variance reduction through baselines, and actor-critic algorithms.
- Actor-Critic Algorithms, Vijay R. Konda and John N. Tsitsiklis, 2000 Advances in Neural Information Processing Systems, Vol. 12 (The MIT Press) - A paper that introduced and formalized the Actor-Critic framework, illustrating how a learned value function (critic) can serve as an effective baseline for policy gradient methods.
- Spinning Up in Deep RL, Joshua Achiam, 2018-2023 (OpenAI) - An accessible online resource from OpenAI that provides practical explanations and implementations of policy gradient methods, including the application of baselines and advantage estimation in deep reinforcement learning.
- High-Dimensional Continuous Control Using Generalized Advantage Estimation, John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel, 2016 International Conference on Learning Representations (ICLR 2016) (OpenReview) DOI: 10.48550/arXiv.1506.02438 - This paper introduces Generalized Advantage Estimation (GAE), a widely adopted technique for reducing variance in policy gradient methods by constructing improved advantage estimates, a direct application of baselines.