Reinforcement Learning

Reinforcement Learning (RL) is an AI paradigm where agents learn by interacting with their environment. It mimics trial-and-error learning, aiming to maximize cumulative rewards. Deep Dive into Deep Reinforcement Learning (DRL) delves further into RL, exploring how neural networks enhance its capabilities. This subfield pioneers self-improving algorithms and their applications, from robotics to gaming.

Courses

• Reinforcement Learning Lecture Series (opens in a new tab) (2021) by DeepMind, in collaboration with University College London, presents a series covering fundamental concepts such as Exploration & Control, Markov-Decision Process (MDPs), Dynamic Programming, Theoretical Foundations of Dynamic Programming Algorithms, Model-free Prediction & Control, Policy-Gradient, Actor-Critic methods, Deep Reinforcement Learning (DRL), and more. If you aspire to learn from a distinguished computer scientist in RL, consider David Silver's course on RL (2008).

• UC Berkeley CS 285 Deep RL Course (opens in a new tab) by Sergey Levine, explores imitation learning, RL intro, policy gradients, actor-critic, and value function methods. It offers insights into the latest advancements and applications in reinforcement learning, enriching understanding in the field. (curriculum)

• Deep Reinforcement Learning Course (opens in a new tab): Hugging Face offers a comprehensive DRL course suitable for beginners to experts. It provides theoretical and practical knowledge, including hands-on exercises using popular libraries like Stable Baselines3 and RL Baselines3 Zoo. The course offers unique environments and is divided into units, starting with foundational topics, such as training a lunar lander on the Moon. Prerequisites include basic Python, linear algebra, and calculus knowledge.

Explainers

• MIT 6.S094: Deep Reinforcement Learning for Motion Planning (opens in a new tab) by Lex Fridman, introduces types of machine learning, the neuron as a computational building block for neural nets, Q-learning, deep reinforcement learning, and the DeepTraffic simulation that utilizes deep reinforcement learning for the motion planning task.

• MIT 6.S191: Reinforcement Learning (opens in a new tab) by Alexander Amini, covers the basics of Reinforcement Learning, including Markov Decision Processes (MDPs), Value Iteration, and Q-learning.

• AlphaGo - How AI mastered the hardest boardgame in history (opens in a new tab): Explains AlphaGo, an AI by DeepMind, defeating the Go world champion. It highlights AI's progress and significance, mentions system limitations, and emphasizes the need for further research to unlock AI's potential.

Articles

• Deep Learning in a Nutshell: Reinforcement Learning (opens in a new tab) by Nvidia offering an intuitive introduction to Reinforcement Learning. It covers key concepts, value and policy functions, and uses analogies and images for clarity.

• AlphaGo (opens in a new tab) (2015), developed by DeepMind, is a remarkable computer program that masters the game of Go using neural networks. Through reinforcement learning, it trained by playing against itself, continuously improving. AlphaGo defeated world champions in different global arenas, arguably becoming the greatest Go player of all time. It also outperformed itself with AlphaGo Zero (2017).

• AlphaZero (opens in a new tab) (2018), developed by DeepMind, autonomously mastered chess, shogi, and Go, surpassing world-champion programs. It combines advanced search trees and neural networks. Despite starting from random play and having no prior knowledge, it excels and exhibits a dynamic, creative playstyle in these games.

• MuZero: Mastering Go, chess, shogi and Atari without rules (opens in a new tab) (2020), developed by DeepMind, excels at Go, chess, shogi, and Atari games without prior knowledge of the rules. It blends AlphaZero's planning with model-free reinforcement learning, predicting relevant future aspects. Setting new benchmarks in reinforcement learning, MuZero matches AlphaZero's superhuman performance.

• Key Papers in Deep RL (opens in a new tab) is a part of OpenAI's Spinning Up in Deep RL project, offers a categorized list of essential papers in deep reinforcement learning. It provides brief descriptions and algorithm references. Maintained by OpenAI.

Guides

• Awesome Reinforcement Learning (opens in a new tab) compiles resources on reinforcement learning, encompassing theory, applications, codes, papers, books, tutorials, and open-source platforms.

• OpenAI Spinning Up (opens in a new tab): An OpenAI educational resource, simplifies deep reinforcement learning exploration. Tailored for aspiring researchers, it introduces RL fundamentals and various algorithm types. The material delves into specific algorithms, such as Trust Region Policy Optimization (TRPO), Proximal Policy Optimization (PPO), and Deep Q-Network (DQN), providing an invaluable foundation for understanding advanced AI applications in today's world.

Reference

• Stable Baselines3 (opens in a new tab) documentation by DLR-RM provides a comprehensive overview of this open source Python library for developing and evaluating reinforcement learning algorithms. It covers installation, tutorials, example use cases, customization of policies and algorithms, development tips, and benchmarks. The docs enable users to effectively leverage Stable Baselines3 for RL research and applications.

• Gym Library (opens in a new tab) offers an expansive catalog of over 1.8K OpenAI Gym environments for reinforcement learning research across diverse domains including Atari, Box2d, MuJoCo, and more. This well-organized interface enables seamless discovery, comparison, and integration of Gym environments into machine learning projects, empowering faster prototyping, benchmarking, and productive reinforcement learning research. Consider Gymnasium for simple interface.

• PettingZoo (opens in a new tab) is a simple, pythonic interface capable of representing general multi-agent reinforcement learning (MARL) problems, includes a wide variety of reference environments, helpful utilities, and tools for creating your own custom environments.

• Minari (opens in a new tab) is a Python API, hosts Offline Reinforcement Learning datasets compatible with the Gymnasium API. Publicly accessible on a Farama GCP bucket, it offers features like episode sampling, trajectory filtering, and dataset generation.

Papers

• Q-learning (opens in a new tab) (1992): Introduced an off-policy reinforcement learning algorithm where agents learn to maximize reward through action value estimates, enabling agents to determine optimal behavior for Markov decision processes. This algorithm is one of the fundamental RL methods.

• Policy invariance under reward transformations: Theory and application to reward shaping (opens in a new tab) (1999): explores reward shaping in RL, aiming to encourage desired behavior in agents. It introduces the policy invariance concept and outlines a method for reward function modification to achieve this goal.

• Learning to Predict by the Methods of Temporal Differences (opens in a new tab) (1988): Presents temporal difference learning, a model-free reinforcement learning algorithm for state value prediction. It updates value estimates based on the discrepancy between predicted and actual rewards.

• Actor-Critic Algorithms (opens in a new tab) (2003): Introduced actor-critic algorithms, uniting policy-based and value-based methods. The actor learns state-action policies, while the critic learns value functions. Various algorithm variants were proposed, including the advantage actor-critic and natural actor-critic.

• Proximal Policy Optimization Algorithms (opens in a new tab) (2017): Presented an actor-critic algorithm for stable deep reinforcement learning using clipped surrogate objectives for the policy update and adaptive KL penalty coefficients for reliable performance. This algorithm enables efficient training of policies in complex environments.

Books

• Grokking Deep Reinforcement Learning (opens in a new tab) by Miguel Morales melds annotated Python code with lucid explanations to delve into Deep Reinforcement Learning (DRL) techniques. It offers insight into algorithm operations and guides you in creating your own DRL agents through evaluative feedback.