Imitation learning---the learning of behavior from demonstrations instead of rewards---moved from Pomerleau's ALVINN driving network and Schaal's humanoid route through Ng and Russell's inverse reinforcement learning, Abbeel and Ng's apprenticeship learning, and Ziebart's maximum entropy to the robot learning from demonstration surveys, Ross's DAgger, Ho and Ermon's generative adversarial imitation, Finn's guided cost learning, and the algorithmic perspective's syntheses. This article presents a narrative review of that arc's canonical line: Pomerleau's 1989 ALVINN, Schaal's 1999 humanoid question, Ng and Russell's 2000 inverse RL, Abbeel and Ng's 2004 apprenticeship learning, Billard and colleagues's 2008 handbook chapter, Ziebart and colleagues's 2008 maximum entropy, Argall and colleagues's 2009 survey, Ross, Gordon, and Bagnell's 2011 DAgger, Ho and Ermon's 2016 GAIL, Finn and colleagues's 2016 guided cost learning, Hussein and colleagues's 2017 survey, and Osa and colleagues's 2018 algorithmic perspective. The review is organized around three themes: the foundations, in which the driving network's demonstrations, the humanoid's question, and the inverse reward's recovery defined the field's two programs; the demonstration's surveys, in which the robot programming's handbook and the LfD's survey systematized the practice; and the deep era, in which the DAgger's covariate correction, the adversarial's discrimination, and the algorithmic perspective's synthesis unified the field. It is concluded that imitation learning is the reward's workaround---and that its arc is the demonstrator's knowledge's transfer from the human's steering to the policy's distributions.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Multi-agent reinforcement learning---the learning of behavior when the environment's other agents learn too---moved from Tan's independent learners and Littman's Markov games framework through the cooperative dynamics' analyses and the surveys' question to the deep era's communication, actor-critics, value decompositions, and the large-scale emergent play of Capture the Flag. This article presents a narrative review of that arc's canonical line: Tan's 1993 independent versus cooperative agents, Littman's 1994 Markov games, Claus and Boutilier's 1998 cooperative dynamics, Hu and Wellman's 1998 framework, Shoham, Powers, and Grenager's 2007 question, Busoniu, Babuska, and De Schutter's 2008 survey, Foerster and colleagues' 2016 learning to communicate, Lowe and colleagues' 2017 multi-agent actor-critic, Sunehag and colleagues' 2018 value-decomposition networks, Rashid and colleagues' 2018 QMIX, Jaderberg and colleagues' 2019 3D multiplayer Capture the Flag, and Hernandez-Leal, Kartal, and Taylor's 2019 survey and critique. The review is organized around three themes: the foundational frames, in which the Markov game's formalization and the non-stationarity's, the coordination's, and the equilibrium's problems defined the field's difficulties; the theory's question, in which the surveys asked what learning among learners is for; and the deep era, in which the communications, the centralized critics, the monotonic factorizations, and the population-scale play made the multi-agent learning practical. It is concluded that multi-agent reinforcement learning is the non-stationarity's discipline---and that its deep era turned the other learners' obstruction into the curriculum's engine.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Imitation learning---the learning of behavior from demonstrations instead of rewards---moved from Pomerleau's ALVINN driving network and Schaal's humanoid route through Ng and Russell's inverse reinforcement learning, Abbeel and Ng's apprenticeship learning, and Ziebart's maximum entropy to the robot learning from demonstration surveys, Ross's DAgger, Ho and Ermon's generative adversarial imitation, Finn's guided cost learning, and the algorithmic perspective's syntheses. This article presents a narrative review of that arc's canonical line: Pomerleau's 1989 ALVINN, Schaal's 1999 humanoid question, Ng and Russell's 2000 inverse RL, Abbeel and Ng's 2004 apprenticeship learning, Billard and colleagues's 2008 handbook chapter, Ziebart and colleagues's 2008 maximum entropy, Argall and colleagues's 2009 survey, Ross, Gordon, and Bagnell's 2011 DAgger, Ho and Ermon's 2016 GAIL, Finn and colleagues's 2016 guided cost learning, Hussein and colleagues's 2017 survey, and Osa and colleagues's 2018 algorithmic perspective. The review is organized around three themes: the foundations, in which the driving network's demonstrations, the humanoid's question, and the inverse reward's recovery defined the field's two programs; the demonstration's surveys, in which the robot programming's handbook and the LfD's survey systematized the practice; and the deep era, in which the DAgger's covariate correction, the adversarial's discrimination, and the algorithmic perspective's synthesis unified the field. It is concluded that imitation learning is the reward's workaround---and that its arc is the demonstrator's knowledge's transfer from the human's steering to the policy's distributions.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Multi-agent reinforcement learning---the learning of behavior when the environment's other agents learn too---moved from Tan's independent learners and Littman's Markov games framework through the cooperative dynamics' analyses and the surveys' question to the deep era's communication, actor-critics, value decompositions, and the large-scale emergent play of Capture the Flag. This article presents a narrative review of that arc's canonical line: Tan's 1993 independent versus cooperative agents, Littman's 1994 Markov games, Claus and Boutilier's 1998 cooperative dynamics, Hu and Wellman's 1998 framework, Shoham, Powers, and Grenager's 2007 question, Busoniu, Babuska, and De Schutter's 2008 survey, Foerster and colleagues' 2016 learning to communicate, Lowe and colleagues' 2017 multi-agent actor-critic, Sunehag and colleagues' 2018 value-decomposition networks, Rashid and colleagues' 2018 QMIX, Jaderberg and colleagues' 2019 3D multiplayer Capture the Flag, and Hernandez-Leal, Kartal, and Taylor's 2019 survey and critique. The review is organized around three themes: the foundational frames, in which the Markov game's formalization and the non-stationarity's, the coordination's, and the equilibrium's problems defined the field's difficulties; the theory's question, in which the surveys asked what learning among learners is for; and the deep era, in which the communications, the centralized critics, the monotonic factorizations, and the population-scale play made the multi-agent learning practical. It is concluded that multi-agent reinforcement learning is the non-stationarity's discipline---and that its deep era turned the other learners' obstruction into the curriculum's engine.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
The Transformer architecture---built on attention rather than recurrence---redrew the landscape of natural language processing and became the substrate of contemporary artificial intelligence. This article presents a narrative review of the architecture's canonical line: Sutskever and colleagues' 2014 sequence-to-sequence learning, Bahdanau and colleagues' 2015 attention alignment, Vaswani and colleagues' 2017 Attention Is All You Need, Devlin and colleagues' 2019 BERT pretraining, Radford and colleagues' 2019 GPT-2, Brown and colleagues' 2020 GPT-3 and few-shot learning, Raffel and colleagues' 2020 T5 transfer, Dosovitskiy and colleagues' 2021 Vision Transformer, Bommasani and colleagues' 2021 foundation-model framing, Hoffmann and colleagues' 2022 Chinchilla scaling laws, Ouyang and colleagues' 2022 InstructGPT alignment, and Touvron and colleagues' 2023 LLaMA openness. The synthesis is organized around three themes: architecture, in which self-attention's parallel sequence processing replaced recurrence and enabled scale; scaling, in which pretraining on text plus parameter growth yielded emergent few-shot capability and then compute-optimal correction; and alignment and access, in which instruction tuning, RL from feedback, and open weights reshaped capability's deployment. It is concluded that the Transformer is machine learning's most consequential architecture to date---its attention mechanism the field's new inductive bias---and that scaling's economics and governance now define its trajectory.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations