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Tag: reinforcement

  • Distributed Reinforcement Learning for Scalable High-Performance Policy Optimization

    Distributed Reinforcement Learning for Scalable High-Performance Policy Optimization Leveraging massive parallelism, asynchronous updates, and multi-machine training to match and exceed human-level performance The post Distributed Reinforcement Learning for Scalable High-Performance Policy Optimization appeared first on Towards Data Science. Sam Black Go to original source

  • Deep Reinforcement Learning: The Actor-Critic Method

    Deep Reinforcement Learning: The Actor-Critic Method Robot friends collaborate to learn to fly a drone The post Deep Reinforcement Learning: The Actor-Critic Method appeared first on Towards Data Science. Vedant Jumle Go to original source

  • The Reinforcement Learning Handbook: A Guide to Foundational Questions

    The Reinforcement Learning Handbook: A Guide to Foundational Questions Simplifying all the concepts required to master reinforcement learning The post The Reinforcement Learning Handbook: A Guide to Foundational Questions appeared first on Towards Data Science. Avishek Biswas Go to original source

  • Deep Reinforcement Learning: 0 to 100

    Deep Reinforcement Learning: 0 to 100 Using RL to teach robots to fly a drone The post Deep Reinforcement Learning: 0 to 100 appeared first on Towards Data Science. Vedant Jumle Go to original source

  • Reinforcement Learning in MDPs with Information-Ordered Policies

    Reinforcement Learning in MDPs with Information-Ordered Policies arXiv:2508.03904v1 Announce Type: new Abstract: We propose an epoch-based reinforcement learning algorithm for infinite-horizon average-cost Markov decision processes (MDPs) that leverages a partial order over a policy class. In this structure, $pi’ leq pi$ if data collected under $pi$ can be used to estimate the performance of $pi’$,…

  • Statistical and Algorithmic Foundations of Reinforcement Learning

    Statistical and Algorithmic Foundations of Reinforcement Learning arXiv:2507.14444v1 Announce Type: new Abstract: As a paradigm for sequential decision making in unknown environments, reinforcement learning (RL) has received a flurry of attention in recent years. However, the explosion of model complexity in emerging applications and the presence of nonconvexity exacerbate the challenge of achieving efficient RL…

  • Reinforcement Learning from Human Feedback, Explained Simply

    Reinforcement Learning from Human Feedback, Explained Simply The one technique that made ChatGPT so smart The post Reinforcement Learning from Human Feedback, Explained Simply appeared first on Towards Data Science. Vyacheslav Efimov Go to original source

  • Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python

    Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python Inspired by AlphaGo’s Move 37 — learn how agents explore, exploit, and win The post Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python appeared first on Towards Data Science. Sarah Schürch Go to original source

  • Reinforcement Learning with Continuous Actions Under Unmeasured Confounding

    Reinforcement Learning with Continuous Actions Under Unmeasured Confounding arXiv:2505.00304v1 Announce Type: new Abstract: This paper addresses the challenge of offline policy learning in reinforcement learning with continuous action spaces when unmeasured confounders are present. While most existing research focuses on policy evaluation within partially observable Markov decision processes (POMDPs) and assumes discrete action spaces, we…

  • Reinforcement Learning from One Example?

    Reinforcement Learning from One Example? Prompt engineering alone won’t get us to production. Fine-tuning is expensive. And reinforcement learning? That’s been reserved for well-funded labs with massive datasets until now. New research from Microsoft and academic collaborators has overturned that assumption. Using Reinforcement Learning with Verifiable Rewards (RLVR) and just a single training example, researchers…

  • Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning

    Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning arXiv:2504.03784v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) has emerged as a key technique for aligning the output of large language models (LLMs) with human preferences. To learn the reward function, most existing RLHF algorithms use the Bradley-Terry model, which relies…

  • On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers

    On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers arXiv:2502.05672v1 Announce Type: new Abstract: This article provides a rigorous analysis of convergence and stability of Episodic Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning and Online Decision Transformers. These algorithms performed competitively across various benchmarks, from games to robotic tasks,…

  • Training Large Language Models: From TRPO to GRPO

    Training Large Language Models: From TRPO to GRPO Deepseek has recently made quite a buzz in the AI community, thanks to its impressive performance at relatively low costs. I think this is a perfect opportunity to dive deeper into how Large Language Models (LLMs) are trained. In this article, we will focus on the Reinforcement Learning…

  • Deep Transfer $Q$-Learning for Offline Non-Stationary Reinforcement Learning

    Deep Transfer $Q$-Learning for Offline Non-Stationary Reinforcement Learning arXiv:2501.04870v1 Announce Type: new Abstract: In dynamic decision-making scenarios across business and healthcare, leveraging sample trajectories from diverse populations can significantly enhance reinforcement learning (RL) performance for specific target populations, especially when sample sizes are limited. While existing transfer learning methods primarily focus on linear regression settings,…