Reinforcement Learning Series
I created this series to make that steep learning curve far less daunting for you. My goal is to trace the evolution of RL chronologically—from its roots in early psychology and physical mechanical machines to digital binary systems and modern mathematical breakthroughs. By breaking down complex concepts with clear visual guides, graphics, and real-world analogies, I hope to demystify RL and give…
This series aims to make the complex topic of Reinforcement Learning more accessible by tracing its evolution from early psychology and physical machines to modern digital systems. Each blog post breaks down key developments chronologically, providing clear visual aids, real-world analogies, and straightforward explanations.
Blog 1 focuses on the psychological foundations laid by Edward Thorndike's Law of Effect (1911), Ivan Pavlov's reinforcement definition (1927), and Donald Hebb's 1949 neuron hypothesis. Blog 2 examines early computational investigations like Alan Turing's pleasure-pain system (1948), Marvin Minsky's SNARCs (1954), and Claude Shannon's 1952 Theseus maze-running mouse.
Blog 3 delves into Richard Bellman's Dynamic Programming and the Bellman Equation (1957), Markov Decision Processes, and Ron Howard's policy iteration method (1960). Blog 4 showcases Arthur Samuel's checkers program (1959) as the first to implement temporal-difference learning, and Donald Michie's MENACE physical matchbox engine that learned to play Noughts and Crosses.
Blog 5 explores Soviet RL research, particularly Mikhail Tsetlin's learning automata (1961) and Narendra and Thathachar's 1974 systematization. Blog 6 covers Harry Klopf's hedonistic neuron concept (1972) and Paul Werbos's 1974 thesis describing backpropagation in Adaptive Dynamic Programming.
Blog 7 highlights Sutton and Barto's 1981 model of classical conditioning, the invention of the Actor-Critic architecture (1983), and Sutton's 1984 dissertation on temporal credit assignment. Blog 8 documents two watershed moments: Sutton's 1988 TD learning formalisation and Chris Watkins's 1989 introduction of Q-Learning, the first model-free, off-policy algorithm.
Blog 9 details TD-Gammon's achievement (1992) of grandmaster backgammon play using neural networks and self-play, and the 1999 options framework introduced by Sutton, Precup, and Singh for temporal abstraction. Blog 10 explains the Policy Gradient Theorem (2000) and Natural Policy Gradient (2002) as precursors to modern trust-region methods.
Blog 11 chronicles Deep RL's rise with DeepMind's DQN (2013/2015) mastering Atari games from pixels using Experience Replay and Target Networks, and DDPG (2015) extending these successes to continuous action spaces. Blog 12 explores TRPO (2015) and PPO (2017) as industry-standard stable training methods, and AlphaGo's historic victory over Lee Sedol in 2016.
Blog 13 introduces Rainbow (2017), a super-agent combining seven DQN improvements, and MuZero (2019), which mastered games like Go and Chess without prior environment rules. Blog 14 focuses on Agent57 (2020) conquering all 57 Atari games, and Gato (2022), a generalist agent proficient in Atari games, image captioning, and robot arm control.
Blog 15 details the Decision Transformer (2021) reframing RL as sequence modeling, and the rise of Reinforcement Learning from Human Feedback (RLHF) with InstructGPT and Direct Preference Optimization (DPO). Finally, Blog 16 examines DeepSeek Math and GRPO (2024), which streamlined the need for a separate critic model to enhance mathematical reasoning in RL models.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
