Stories with Tag TD Learning

• Richard Sutton and Andrew Barto Win 2024 Turing Award

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  Posted: 2025-03-05 10:03:31

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  Richard Sutton and Andrew Barto have been awarded the 2024 ACM A.M. Turing Award for their foundational contributions to reinforcement learning (RL). Their collaborative work, spanning decades and culminating in the influential textbook Reinforcement Learning: An Introduction, established key algorithms, conceptual frameworks, and theoretical understandings that propelled RL from a niche topic to a central area of artificial intelligence. Their research laid the groundwork for numerous breakthroughs in fields like robotics, game playing, and resource management, enabling the development of intelligent systems capable of learning through trial and error.

  The Association for Computing Machinery (ACM) has bestowed the prestigious 2024 A.M. Turing Award, often referred to as the "Nobel Prize of Computing," upon Richard S. Sutton and Andrew G. Barto for their groundbreaking and foundational contributions to the field of reinforcement learning (RL). Their collaborative work, spanning several decades, has revolutionized the way computers learn and interact with their environment, paving the way for advancements in artificial intelligence that were previously relegated to the realm of science fiction.

  Sutton and Barto's research has been instrumental in establishing reinforcement learning as a distinct and powerful paradigm within machine learning. Their seminal textbook, "Reinforcement Learning: An Introduction," initially published in 1998 and later updated in a second edition in 2018, serves as the definitive guide to the field. This comprehensive work has not only educated generations of researchers and practitioners but has also codified the core principles and algorithms that underpin contemporary reinforcement learning.

  The award specifically recognizes their contributions to the development of temporal-difference learning, a crucial aspect of reinforcement learning that allows agents to learn from ongoing experience without waiting for a final outcome. This methodology enables machines to adapt to dynamic environments and make predictions about future rewards, leading to more efficient and effective learning processes. Their exploration of policy gradient methods has also been pivotal, enabling the direct optimization of control policies within reinforcement learning systems. This further refines the learning process, allowing agents to learn optimal strategies for interacting with complex environments.

  The impact of their work extends far beyond academia. Reinforcement learning, thanks to their pioneering research, is now employed in a diverse array of practical applications. These include robotics, where it allows robots to learn complex motor skills and navigate challenging terrains; game playing, enabling AI agents to achieve superhuman performance in games like Go and chess; resource management, where it optimizes energy consumption and distribution in complex systems; and personalized recommendations, where it tailors online experiences to individual user preferences.

  The Turing Award is a testament to the profound influence Sutton and Barto have exerted on the field of computer science. Their decades-long dedication to the advancement of reinforcement learning has not only enriched our understanding of machine intelligence but has also opened doors to a future where intelligent systems can seamlessly integrate into our lives, solving complex problems and enhancing human capabilities in myriad ways. Their contributions have been fundamental to the ongoing evolution of artificial intelligence and will continue to inspire future generations of researchers and innovators.

  • Richard Sutton
  • Andrew Barto
  • Turing Award
  • ACM
  • Association for Computing Machinery
  • reinforcement learning
  • artificial intelligence
  • AI
  • Computer Science
  • Temporal-Difference Learning
  • TD Learning
  • 2024 Turing Award
  • Awards
  • machine learning

  Summary of Comments ( 53 )
  https://news.ycombinator.com/item?id=43264847

  Verbose tl;dr

  Hacker News commenters overwhelmingly praised Sutton and Barto's contributions to reinforcement learning, calling their book the "bible" of the field and highlighting its impact on generations of researchers. Several shared personal anecdotes about using their book, both in academia and industry. Some discussed the practical applications of reinforcement learning, ranging from robotics and game playing to personalized recommendations and resource management. A few commenters delved into specific technical aspects, mentioning temporal-difference learning and policy gradients. There was also discussion about the broader significance of the Turing Award and its recognition of fundamental research.

  The Hacker News post titled "Richard Sutton and Andrew Barto Win 2024 Turing Award" has generated several comments discussing the significance of their work in reinforcement learning. Many commenters express admiration for Sutton and Barto's foundational contributions, particularly their textbook "Reinforcement Learning: An Introduction," which is widely considered the canonical text in the field. Several people share personal anecdotes about how the book influenced their careers or helped them understand complex concepts. The clarity and accessibility of the book are frequently praised.

  Some comments delve into the technical details of reinforcement learning, highlighting the importance of temporal difference learning, a key concept pioneered by Sutton. They discuss its impact on various fields, including robotics, game playing, and artificial intelligence more broadly. There's acknowledgement of the long trajectory of research in this area and the dedication required to bring these ideas to fruition.

  A few commenters mention the relative lack of public awareness of reinforcement learning compared to other areas of AI, like deep learning, despite its profound impact. They suggest this might be due to the less visually spectacular nature of reinforcement learning applications.

  A recurring theme is the well-deserved nature of the award, with many expressing their respect for Sutton and Barto's intellectual contributions and their influence on subsequent generations of researchers. Some comments also speculate on the future of reinforcement learning and its potential to solve even more complex problems. There's a sense of excitement about the ongoing developments in the field and the possibilities that lie ahead. Finally, several commenters express their congratulations to the award winners.

• Reinforcement Learning: An Overview

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  Posted: 2025-02-02 17:20:21

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  Reinforcement learning (RL) is a machine learning paradigm where an agent learns to interact with an environment by taking actions and receiving rewards. The goal is to maximize cumulative reward over time. This overview paper categorizes RL algorithms based on key aspects like value-based vs. policy-based approaches, model-based vs. model-free learning, and on-policy vs. off-policy learning. It discusses fundamental concepts such as the Markov Decision Process (MDP) framework, exploration-exploitation dilemmas, and various solution methods including dynamic programming, Monte Carlo methods, and temporal difference learning. The paper also highlights advanced topics like deep reinforcement learning, multi-agent RL, and inverse reinforcement learning, along with their applications across diverse fields like robotics, game playing, and resource management. Finally, it identifies open challenges and future directions in RL research, including improving sample efficiency, robustness, and generalization.

  The arXiv preprint "Reinforcement Learning: An Overview" offers a comprehensive and meticulously detailed survey of the field of reinforcement learning (RL). It begins by establishing the fundamental principles of RL, defining its core components: the agent, the environment, the state, the action, the reward, and the policy. It emphasizes the iterative nature of RL, where agents learn through trial-and-error interactions with their environment, aiming to maximize cumulative rewards over time. The paper meticulously distinguishes between various learning paradigms, including model-based RL, where agents construct an internal model of the environment, and model-free RL, where agents learn directly from experience without explicitly modeling the environment. Furthermore, it delves into the crucial distinction between on-policy learning, which utilizes data generated by the current policy being followed, and off-policy learning, which leverages data generated by potentially different policies.

  The overview then systematically categorizes and elaborates on a wide spectrum of RL algorithms. It explores classic methods like dynamic programming, highlighting its reliance on complete environment knowledge, and Monte Carlo methods, which estimate value functions through repeated sampling of complete episodes. The paper subsequently delves into temporal-difference learning, a pivotal concept in modern RL, explaining its mechanisms for bootstrapping value estimates from future predictions. It dissects prominent algorithms like Q-learning and SARSA, elucidating their differences in policy evaluation and update strategies.

  The survey proceeds to address the complexities of function approximation in RL, explaining how neural networks can represent value functions and policies, enabling the handling of high-dimensional state and action spaces. It discusses the challenges of combining deep learning with RL, including the issues of stability and convergence. The paper then introduces policy gradient methods, a powerful class of algorithms that directly optimize policy parameters, contrasting them with value-based methods. It describes prominent policy gradient algorithms like REINFORCE and actor-critic methods, highlighting the role of the critic in estimating value functions to improve policy updates.

  Further expanding its scope, the overview explores advanced topics such as exploration-exploitation dilemmas, explaining various strategies for balancing the need to explore new actions with the desire to exploit learned knowledge. It discusses techniques like epsilon-greedy, softmax exploration, and upper confidence bound (UCB). The paper also delves into the complexities of learning in multi-agent environments, where multiple agents interact and learn simultaneously, introducing concepts like cooperative, competitive, and mixed-motive settings. It explores different approaches to multi-agent RL, including independent learners, joint action learners, and communication-based methods.

  Finally, the overview concludes by highlighting the vast array of applications for reinforcement learning across diverse domains, including robotics, game playing, resource management, and personalized recommendations. It emphasizes the continued rapid advancements in the field and points towards promising future research directions, such as improving sample efficiency, addressing the challenges of generalization, and developing more robust and scalable RL algorithms. The paper provides a thorough and invaluable resource for anyone seeking a comprehensive understanding of the field of reinforcement learning, from its foundational principles to its cutting-edge advancements.

  • reinforcement learning
  • machine learning
  • artificial intelligence
  • Overview
  • Introduction
  • RL
  • AI
  • Markov Decision Process
  • MDP
  • dynamic programming
  • Monte Carlo methods
  • Temporal Difference Learning
  • TD Learning
  • Q-Learning
  • SARSA
  • Deep Reinforcement Learning
  • Deep RL
  • neural networks
  • Policy Gradient Methods
  • Actor-Critic
  • Value Function Approximation
  • Exploration vs Exploitation
  • Reward Function
  • Agent
  • Environment
  • Control Theory
  • Robotics
  • Game Playing
  • optimization
  • survey

  Summary of Comments ( 9 )
  https://news.ycombinator.com/item?id=42910028

  Verbose tl;dr

  HN users discuss various aspects of Reinforcement Learning (RL). Some express skepticism about its real-world applicability outside of games and simulations, citing issues with reward function design, sample efficiency, and sim-to-real transfer. Others counter with examples of successful RL deployments in robotics, recommendation systems, and resource management, while acknowledging the challenges. A recurring theme is the complexity of RL compared to supervised learning, and the need for careful consideration of the problem domain before applying RL. Several commenters highlight the importance of understanding the underlying theory and limitations of different RL algorithms. Finally, some discuss the potential of combining RL with other techniques, such as imitation learning and model-based approaches, to overcome some of its current limitations.

  The Hacker News post titled "Reinforcement Learning: An Overview" (linking to an arXiv paper) has generated a moderate number of comments, mostly focusing on the practical applications and limitations of reinforcement learning (RL), rather than the specifics of the linked paper. Several commenters offer their perspectives on the current state and future of RL, drawing on personal experience and general industry trends.

  One compelling line of discussion revolves around the gap between the academic hype surrounding RL and its real-world applicability. One commenter, seemingly experienced in the field, points out that RL is often viewed as a "silver bullet" in academia, while in practice it's often outperformed by simpler, more traditional methods. They emphasize the importance of carefully evaluating whether RL is truly the best tool for a given problem, suggesting that its complexity often outweighs its benefits. This sentiment is echoed by others who note the difficulty of setting up and tuning RL systems, particularly in scenarios with real-world constraints.

  Another commenter highlights the specific challenges associated with applying RL in robotics, citing the need for extensive simulation and the difficulty of transferring learned behaviors to real-world robots. They contrast this with the relative success of supervised learning in other areas of robotics, suggesting that RL's current limitations hinder its widespread adoption in this domain.

  There's also a discussion about the potential of RL in areas like chip design and scientific discovery. One comment specifically mentions the possibility of using RL to optimize complex systems like particle accelerators, but acknowledges the significant hurdles involved in applying RL to such intricate and poorly understood systems.

  A few comments touch on more technical aspects, discussing specific RL algorithms and techniques. One commenter mentions the limitations of Q-learning in continuous action spaces and points to the potential of policy gradient methods as a more suitable alternative. Another briefly discusses the challenges of reward shaping, a crucial aspect of RL where defining the appropriate reward function can significantly impact the performance of the learning agent.

  Overall, the comments reflect a measured perspective on RL, acknowledging its potential while also emphasizing its current limitations and the need for careful consideration before applying it to real-world problems. The discussion provides valuable insights from practitioners and researchers who offer a nuanced view of the field, moving beyond the often-optimistic portrayal of RL in academic circles.