reinforcement learning
Machine learning approach where an agent learns a policy by receiving rewards or penalties from environmental feedback. Underlies game-playing and robotic control systems.
5 milestones
Reinforcement learning framework proposed to model T-cell adaptive immune response
In 2021, researchers published in Physical Review Research a theoretical framework proposing that T-cell receptor signalling during adaptive immunity can be formally described as a reinforcement learning process, connecting immunological learning to established machine-learning theory.
OpenAI Released OpenAI Gym, a Toolkit for Reinforcement Learning Research
In April 2016, OpenAI publicly released OpenAI Gym, an open-source toolkit providing a standardised collection of environments for developing and benchmarking reinforcement learning algorithms, lowering the barrier to reproducible RL research.
DeepMind Publishes AlphaGo, a Deep Reinforcement Learning System That Defeated Professional Go Players
In January 2016, researchers at Google DeepMind published a paper in Nature describing AlphaGo, a system combining deep convolutional neural networks with Monte Carlo tree search and reinforcement learning that defeated the European Go champion Fan Hui 5–0, marking the first time a computer program had beaten a professional Go player at full-board Go.
OpenAI Founded
In December 2015, a group of technology investors and researchers (including Greg Brockman, Ilya Sutskever, Wojciech Zaremba, John Schulman, Elon Musk, and Sam Altman) announced the founding of OpenAI, a non-profit artificial intelligence research laboratory in San Francisco, with approximately one billion US dollars in pledged funding.
TD-Gammon Developed by Gerald Tesauro at IBM
In 1992, Gerald Tesauro at IBM Thomas J. Watson Research Center developed TD-Gammon, a backgammon program that trained itself through self-play using temporal-difference learning applied to a multilayer neural network, reaching a standard of play close to that of strong human experts.